Publishing type：Research paper (scientific journal)  

DOI： 10.1109/TMI.2025.3557528

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Language：English   Publishing type：Research paper (scientific journal)  

The development of facial expression recognition (FER) and facial expression generation (FEG) systems is essential to enhance human-robot interactions (HRI). The facial action coding system is widely used in FER and FEG tasks, as it offers a framework to relate the action of facial muscles and the resulting facial motions to the execution of facial expressions. However, most FER and FEG studies are based on measuring and analyzing facial motions, leaving the facial muscle component relatively unexplored. This study introduces a novel framework using surface electromyography (sEMG) signals from facial muscles to recognize facial expressions and estimate the displacement of facial keypoints during the execution of the expressions. For the facial expression recognition task, we studied the coordination patterns of seven muscles, expressed as three muscle synergies extracted through non-negative matrix factorization, during the execution of six basic facial expressions. Muscle synergies are groups of muscles that show coordinated patterns of activity, as measured by their sEMG signals, and are hypothesized to form the building blocks of human motor control. We then trained two classifiers for the facial expressions based on extracted features from the sEMG signals and the synergy activation coefficients of the extracted muscle synergies, respectively. The accuracy of both classifiers outperformed other systems that use sEMG to classify facial expressions, although the synergy-based classifier performed marginally worse than the sEMG-based one (classification accuracy: synergy-based 97.4%, sEMG-based 99.2%). However, the extracted muscle synergies revealed common coordination patterns between different facial expressions, allowing a low-dimensional quantitative visualization of the muscle control strategies involved in human facial expression generation. We also developed a skin-musculoskeletal model enhanced by linear regression (SMSM-LRM) to estimate the displacement of facial keypoints during the execution of a facial expression based on sEMG signals. Our proposed approach achieved a relatively high fidelity in estimating these displacements (NRMSE 0.067). We propose that the identified muscle synergies could be used in combination with the SMSM-LRM model to generate motor commands and trajectories for desired facial displacements, potentially enabling the generation of more natural facial expressions in social robotics and virtual reality.

DOI： 10.3389/fbioe.2025.1490919

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DOI： 10.1016/j.neunet.2024.106899

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DOI： 10.1152/jn.00011.2024

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Language：English   Publishing type：Research paper (scientific journal)  

An essential aspect of human motor learning is the formation of inverse models, which map desired actions to motor commands. Inverse models can be learned by adjusting parameters in neural circuits to minimize errors in the performance of motor tasks through gradient descent. However, the theory of gradient descent establishes limits on the learning speed. Specifically, the eigenvalues of the Hessian of the error surface around a minimum determine the maximum speed of learning in a task. Here, we use this theoretical framework to analyze the speed of learning in different inverse model learning architectures in a set of isometric arm-reaching tasks. We show theoretically that, in these tasks, the error surface and, thus the speed of learning, are determined by the shapes of the force manipulability ellipsoid of the arm and the distribution of targets in the task. In particular, rounder manipulability ellipsoids generate a rounder error surface, allowing for faster learning of the inverse model. Rounder target distributions have a similar effect. We tested these predictions experimentally in a quasi-isometric reaching task with a visuomotor transformation. The experimental results were consistent with our theoretical predictions. Furthermore, our analysis accounts for the speed of learning in previous experiments with incompatible and compatible virtual surgery tasks, and with visuomotor rotation tasks with different numbers of targets. By identifying aspects of a task that influence the speed of learning, our results provide theoretical principles for the design of motor tasks that allow for faster learning.

DOI： 10.1016/j.neunet.2023.10.049

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Publishing type：Research paper (scientific journal)  

DOI： 10.48550/arXiv.2404.15309

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Language：English   Publishing type：Research paper (scientific journal)  

Bayesian learning provides a unified skeleton to solve the electrophysiological source imaging task. From this perspective, existing source imaging algorithms utilize the Gaussian assumption for the observation noise to build the likelihood function for Bayesian inference. However, the electromagnetic measurements of brain activity are usually affected by miscellaneous artifacts, leading to a potentially non-Gaussian distribution for the observation noise. Hence the conventional Gaussian likelihood model is a suboptimal choice for the real-world source imaging task. In this study, we aim to solve this problem by proposing a new likelihood model which is robust with respect to non-Gaussian noises. Motivated by the robust maximum correntropy criterion, we propose a new improper distribution model concerning the noise assumption. This new noise distribution is leveraged to structure a robust likelihood function and integrated with hierarchical prior distributions to estimate source activities by variational inference. In particular, the score matching is adopted to determine the hyperparameters for the improper likelihood model. A comprehensive performance evaluation is performed to compare the proposed noise assumption to the conventional Gaussian model. Simulation results show that, the proposed method can realize more precise source reconstruction by designing known ground-truth. The real-world dataset also demonstrates the superiority of our new method with the visual perception task. This study provides a new backbone for Bayesian source imaging, which would facilitate its application using real-world noisy brain signal.

DOI： 10.48550/arXiv.2408.14843

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Language：English   Publishing type：Research paper (scientific journal)  

Social activities are likely to cause effects or reactivity in the brains of the people involved in collaborative social situations. This study assesses a new method, Tigramite, for time domain analysis of directed causality between the prefrontal cortex (PFC) of persons in such situations. An experimental situation using hyperscanning EEG was applied while individuals led and followed each other in finger-tapping rhythms. This structured task has a long duration and a high likelihood of inter-brain causal reactions in the prefrontal cortices. Tigramite is a graph-based causal discovery method to identify directed causal relationships in observational time series. Tigramite was used to analyze directed causal connections within and between the PFC. Significantly directed causality within and between brains could be detected during the social interactions. This is the first empirical evidence the Tigramite can reveal inter- and intra-brain-directed causal effects in hyperscanning EEG time series. The findings are promising for further studies of causality in neural networks during social activities using Tigramite on EEG in the time domain.

DOI： 10.3389/fnins.2024.1305918

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Language：English   Publishing type：Research paper (scientific journal)  

BACKGROUND: The Swedish Universities Scales of Personality (SSP) is a personality measurement tool with a short test battery of high psychometric quality, previously not availiable in Japanese. METHODS: We translated the SSP into Japanese and administered it to 103 Japanese nationals. For 11 of the 13 SSP scales in the Japanese version of the SSP (SSP-J11), the Cronbach's alpha ranged from 0.50 to 0.82 with good internal scale reliability. RESULTS: A principal factor analysis replicated the previous work by identifying the same three principal dimensions of Neuroticism, Aggression, and Extraversion factors. CONCLUSION: The resulting three-factor SSP-J11 shows acceptable reliability and should provide informative insights about personality traits in research and clinical practice in a Japanese context.

DOI： 10.48101/ujms.v129.10349

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

Abstract

Unlike ballistic arm movements such as reaching, the contribution of depth information to the performance of manual tracking movements is unclear. Thus, to understand how the brain handles information, we investigated how a required movement along the depth axis would affect behavioral tracking performance, postulating that it would be affected by the amount of depth movement. We designed a visually guided planar tracking task that requires movement on three planes with different depths: a fronto-parallel plane called ROT (0), a sagittal plane called ROT (90), and a plane rotated by 45° with respect to the sagittal plane called ROT (45). Fifteen participants performed a circular manual tracking task under binocular and monocular visions in a three-dimensional (3D) virtual reality space. As a result, under binocular vision, ROT (90), which required the largest depth movement among the tasks, showed the greatest error in 3D. Similarly, the errors (deviation from the target path) on the depth axis revealed significant differences among the tasks. Under monocular vision, significant differences in errors were observed only on the lateral axis. Moreover, we observed that the errors in the lateral and depth axes were proportional to the required movement on these axes under binocular vision and confirmed that the required depth movement under binocular vision determined depth error independent of the other axes. This finding implies that the brain may independently process binocular vision information on each axis. Meanwhile, the required depth movement under monocular vision was independent of performance along the depth axis, indicating an intractable behavior. Our findings highlight the importance of handling depth movement, especially when a virtual reality situation, involving tracking tasks, is generated.

DOI： 10.1038/s41598-023-48869-w

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Other Link： https://www.nature.com/articles/s41598-023-48869-w

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DOI： 10.1109/TBME.2023.3246599

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Language：English   Publishing type：Research paper (scientific journal)  

Electroencephalogram (EEG) channel optimization can reduce redundant information and improve EEG decoding accuracy by selecting the most informative channels. This article aims to investigate the universality regarding EEG channel optimization in terms of how well the selected EEG channels can be generalized to different participants. In particular, this study proposes a sparse logistic regression (SLR)-based EEG channel optimization algorithm using a non-zero model parameter ranking method. The proposed channel optimization algorithm was evaluated in both individual analysis and group analysis using the raw EEG data, compared with the conventional channel selection method based on the correlation coefficients (CCS). The experimental results demonstrate that the SLR-based EEG channel optimization algorithm not only filters out most redundant channels (filters 75-96.9% of channels) with a 1.65-5.1% increase in decoding accuracy, but it can also achieve a satisfactory level of decoding accuracy in the group analysis by employing only a few (2-15) common EEG electrodes, even for different participants. The proposed channel optimization algorithm can realize better universality for EEG decoding, which can reduce the burden of EEG data acquisition and enhance the real-world application of EEG-based brain-computer interface (BCI).

DOI： 10.3390/bioengineering10060664

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Language：English   Publishing type：Research paper (scientific journal)  

The Partial Least Square Regression (PLSR) method has shown admirable competence for predicting continuous variables from inter-correlated electrocorticography signals in the brain-computer interface. However, PLSR is essentially formulated with the least square criterion, thus, being considerably prone to the performance deterioration caused by the brain recording noises. To address this problem, this study aims to propose a new robust variant for PLSR. To this end, the maximum correntropy criterion (MCC) is utilized to propose a new robust implementation of PLSR, called Partial Maximum Correntropy Regression (PMCR). The half-quadratic optimization is utilized to calculate the robust projectors for the dimensionality reduction, and the regression coefficients are optimized by a fixed-point optimization method. The proposed PMCR is evaluated with a synthetic example and a public electrocorticography dataset under three performance indicators. For the synthetic example, PMCR realized better prediction results compared with the other existing methods. PMCR could also abstract valid information with a limited number of decomposition factors in a noisy regression scenario. For the electrocorticography dataset, PMCR achieved superior decoding performance in most cases, and also realized the minimal neurophysiological pattern deterioration with the interference of the noises. The experimental results demonstrate that, the proposed PMCR could outperform the existing methods in a noisy, inter-correlated, and high-dimensional decoding task. PMCR could alleviate the performance degradation caused by the adverse noises and ameliorate the electrocorticography decoding robustness for the brain-computer interface.

DOI： 10.3389/fnins.2023.1213035

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/IJCNN54540.2023.10191293

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Other Link： https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2023.html#LiCYYK23

Publishing type：Research paper (scientific journal)  

DOI： 10.48550/arXiv.2302.00082

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Hindawi Limited  

Background. Dark chocolate has attracted attention for its potential for cognitive improvement. Though some reports indicate that dark chocolate is good for cognitive function, others raise doubts. This inconsistency in past results reflecting the relationship between dark chocolate and cognitive function indicates the potential existence of factors that mediate between dark chocolate intake and cognitive function. Methods. With the hypothesis that fatigue may be one such mediating factor, we performed a four-week randomized control study to seek a link between dark chocolate consumption, cognitive function, fatigue, and the brain in middle-aged adults. Results. We found that dark chocolate reduced mental and physical fatigue, and a path analysis revealed that it enhanced vitality, executive function, memory, and gray matter volume both directly and indirectly. Fatigue reduction was also associated with an improvement in physical function, which had a positive impact on emotional functioning, relief of bodily pain, and social functioning. Conclusions. Our results suggest that dark chocolate may help reduce fatigue in individuals, leading to improvements in brain health and various cognitive functions as well as in quality of life.

DOI： 10.1155/2022/6021811

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Other Link： http://downloads.hindawi.com/journals/bn/2022/6021811.xml

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This study addresses time intervals during robot control that dominate user satisfaction and factors of robot movement that induce satisfaction. We designed a robot control system using electromyography signals. In each trial, participants were exposed to different experiences as the cutoff frequencies of a low-pass filter were changed. The participants attempted to grab a bottle by controlling a robot. They were asked to evaluate four indicators (stability, imitation, response time, and movement speed) and indicate their satisfaction at the end of each trial by completing a questionnaire. The electroencephalography signals of the participants were recorded while they controlled the robot and responded to the questionnaire. Two independent component clusters in the precuneus and postcentral gyrus were the most sensitive to subjective evaluations. For the moment that dominated satisfaction, we observed that brain activity exhibited significant differences in satisfaction not immediately after feeding an input but during the later stage. The other indicators exhibited independently significant patterns in event-related spectral perturbations. Comparing these indicators in a low-frequency band related to the satisfaction with imitation and movement speed, which had significant differences, revealed that imitation covered significant intervals in satisfaction. This implies that imitation was the most important contributing factor among the four indicators. Our results reveal that regardless of subjective satisfaction, objective performance evaluation might more fully reflect user satisfaction.

DOI： 10.3390/s23010277

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Language：English   Publishing type：Research paper (scientific journal)  

Leading and following is about synchronizing and joining actions in accordance with the differences that the leader and follower roles provide. The neural reactivity representing these roles was measured in an explorative fMRI-study, where two persons lead and followed each other in finger tapping using simple, individual, pre-learnt rhythms. All participants acted both as leader and follower. Neural reactivity for both lead and follow related to social awareness and adaptation distributed over the lateral STG, STS and TPJ. Reactivity for follow contrasted with lead mostly reflected sensorimotor and rhythmic processing in cerebellum IV, V, somatosensory cortex and SMA. During leading, as opposed to following, neural reactivity was observed in the insula and bilaterally in the superior temporal gyrus, pointing toward empathy, sharing of feelings, temporal coding and social engagement. Areas for continuous adaptation, in the posterior cerebellum and Rolandic operculum, were activated during both leading and following. This study indicated mutual adaptation of leader and follower during tapping and that the roles gave rise to largely similar neuronal reactivity. The differences between the roles indicated that leading was more socially focused and following had more motoric- and temporally related neural reactivity.

DOI： 10.1080/17470919.2023.2189615

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Language：English   Publishing type：Research paper (scientific journal)  

Neural circuits connecting the cerebellum with the cerebral cortex are important for both motor and cognitive functions. Therefore, assessment of cerebellar function is clinically important for patients with various motor and cognitive dysfunctions. Cerebellum-dependent motor learning has been studied using various tasks. The most widely used tasks are visuomotor adaptation tasks, in which subjects are required to make movements in two dimensions. Studies using simpler tasks of one-dimensional movement, which are easier for patients with motor problems to perform, have suggested that anticipatory responses in these tasks are useful to evaluate cerebellum-dependent motor control or motor learning. In this study, we examined whether the motor learning process can be evaluated in a simple loading task. Using space interface device for artificial reality (SPIDAR), a constant downward force was loaded to subjects' hands in a predictable condition, and the vertical movement of the hand was recorded. The hand deflection from the initial position was displayed on a screen for visual feedback information. We examined effects of repeated loading task training (90 times) on hand movements, by analyzing a small upward movement just before loading (anticipatory response) and a large downward movement after loading in each trial. We found that the repeated training lowered the time constant of upward movement and reduced the amplitude and time-to-peak of downward movement. These training effects were maintained into the next day. Furthermore, we found that loading task training with eyes closed was also effective, which indicates that proprioceptive information is enough for improvement of performance.

DOI： 10.1007/s00221-022-06500-w

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DOI： 10.1101/2022.08.24.505193

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3389/fnins.2022.867480

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Publishing type：Research paper (scientific journal)   Publisher：{MDPI} {AG}  

DOI： 10.3390/app12083772

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3390/mi13020293

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Language：English   Publishing type：Research paper (scientific journal)  

UNLABELLED: Murata S, Koike Y, Kasukawa Y, Saito K, Okada K, Kudo D, Shimada Y, Miyakoshi N. Contralaterally controlled functional electrical stimulation immediately improves hand function. Jpn J Compr Rehabil Sci 2022; 13: 26-30. OBJECTIVE: The purpose of this study was to investigate the immediate effects of contralaterally controlled functional electrical stimulation (CCFES) on upper limb function in stroke patients. METHODS: CCFES and mirror therapy (MT) exercises were conducted for 13 stroke patients at least 4 weeks post-onset. A sufficient interval of at least 24 hours was left between the two types of rehabilitation exercises. Before treatment and immediately after each training session, grip strength, Fugl-Meyer Assessment for Upper Extremity (FMA-UE) score and FMA-UE subscores for the shoulder/elbow/forearm, wrist, hand, and coordination were evaluated. RESULTS: Grip strength, FMA-UE and FMA-UE shoulder/elbow/forearm, wrist, and coordination did not differ significantly after CCFES and MT compared to before therapy. FMA-UE hand did not change significantly after MT compared to before therapy, but it improved significantly after CCFES (p = 0.013). CONCLUSION: CCFES for the upper extremities immediately improves hand function and may be effective in maintaining and improving patients' motivation for rehabilitation treatment.

DOI： 10.11336/jjcrs.13.26

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Augmented feedback provided by a coach or augmented reality system can facilitate the acquisition of a motor skill. Verbal instructions and visual aids can be effective in providing feedback about the kinematics of the desired movements. However, many skills require mastering not only kinematic, but also complex kinetic patterns, for which feedback is harder to convey. Here, we propose the electromyography (EMG) space similarity feedback, which may indirectly convey kinematic and kinetic feedback by comparing the muscle activations of the learner and an expert in the task. The EMG space similarity feedback is a score that reflects how well a set of muscle synergies extracted from the expert can reconstruct the learner's EMG when performing the task. We tested the EMG space similarity feedback in a virtual bimanual polishing task that uses a robotic system to simulate the dynamics of a real polishing operation. We measured the expert's and learner's EMG from eight muscles in each arm during the real and virtual polishing tasks, respectively. The goal of the virtual task was to smoothen the surface of a virtual object. Therefore, we defined performance in the task as the smoothness of the object at the end of a trial. We separated learners into real feedback and null feedback groups to assess the effects of the EMG space similarity feedback. The real and null feedback groups received veridic and no EMG space similarity feedback, respectively. Subjects participated in five training sessions on different days, and we evaluated their performance on each day. Subjects in both groups were able to increase smoothness throughout the training sessions, with no significant differences between groups. However, subjects in the real feedback group were able to improve in the EMG space similarity score to a significantly greater extent than the null feedback group. Additionally, subjects in the real feedback group produced muscle activations that became increasingly consistent with an important muscle synergy found in the expert. Our results indicate that the EMG space similarity feedback promotes acquiring expert-like muscle activation patterns, suggesting that it may assist in the acquisition of complex motor skills.

DOI： 10.3389/fnhum.2022.805867

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UNLABELLED: Sakai R, Koike Y, Saito K, Matsunaga T, Shimada Y, Miyakoshi N. Aphasia testing (auditory comprehension domain) using a new eye-tracking system in healthy participants. Jpn J Compr Rehabil Sci 2022; 13: 31-35. OBJECTIVE: We administered a conventional pointing-method test with eye-tracking to evaluate items associated with auditory comprehension and examined the concordance between the obtained results. METHODS: The enrolled participants were 10 healthy volunteers. We performed tests after extracting auditory comprehension items from the SLTA, the WAB, and the Supplementary tests for the SLTA using the eye-tracking system and the pointing method. RESULTS: The mean test duration was 9 min 51 s ± 1 min 41 s (mean ± SD), and the percentage of correct answers was 100% and in perfect agreement for the pointing method and the eye-tracking system. The mean response time was 0.96 ± 0.36 s for the pointing method and -0.39 ± 0.21 s for the eye-tracking system. Hence, the latter was faster than the former, and examinees completed their responses before listening to the end of the questions. CONCLUSION: The new eye-tracking system makes it possible to perform aphasia tests (auditory comprehension items) comparable to the conventional pointing method.

DOI： 10.11336/jjcrs.13.31

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For upper limb amputees, wearing a myoelectric prosthetic hand is the only way for them to continue normal life. Even until now, the proposal of a high-precision and natural performance real-time control system based on surface electromyography (sEMG) signals is still challenging. Researchers have proposed many strategies for motion classification or regression prediction tasks based on sEMG signals. However, most of them have been limited to offline analysis only. There are even few papers on real-time control based on deep learning models, almost all of which are about motion classification. Rare studies tried to use deep learning-based regression models in real-time control systems for multi-joint angle estimation via sEMG signals. This paper proposed a CW-CNN regression model-based real-time control system for virtual hand control. We designed an Adaptive Kalman Filter to smooth the joint angles output before sending them as control commands to control a virtual hand. Eight healthy participants were invited, and three sessions experiments were conducted on two different days for all of them. During the real-time experiment, we analyzed the joint angles estimation accuracy and computational latency. Moreover, target achievement control (TAC) test was applied to emphasize motion regression in real-time. The experimental results show that the proposed control system has high precision for 3-DOFs motion regression in simultaneously, and the system remains stable and low computational latency. In the future, the proposed real-time control system can be applied to actual prosthetic hand.

DOI： 10.3389/fnbot.2022.1072365

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Muscle synergy analysis via surface electromyography (EMG) is useful to study muscle coordination in motor learning, clinical diagnosis, and neurorehabilitation. However, current methods to extract muscle synergies in the upper limb suffer from two major issues. First, the necessary normalization of EMG signals is performed via maximum voluntary contraction (MVC), which requires maximal isometric force production in each muscle. However, some individuals with motor impairments have difficulties producing maximal effort in the MVC task. In addition, the MVC is known to be highly unreliable, with widely different forces produced in repeated measures. Second, synergy extraction in the upper limb is typically performed with a multidirection reaching task. However, some participants with motor impairments cannot perform this task because it requires precise motor control. In this study, we proposed a new isometric rotating task that does not require precise motor control or large forces. In this task, participants maintain a cursor controlled by the arm end-point force on a target that rotates at a constant angular velocity at a designated force level. To relax constraints on motor control precision, the target is widened and blurred. To obtain a reference EMG value for normalization without requiring maximal effort, we estimated a linear relationship between joint torques and muscle activations. We assessed the reliability of joint torque normalization and synergy extraction in the rotating task in young neurotypical individuals. Compared with normalization with MVC, joint torque normalization allowed reliable EMG normalization at low force levels. In addition, the extraction of synergies was as reliable and more stable than with the multidirection reaching task. The proposed rotating task can, therefore, be used in future motor learning, clinical diagnosis, and neurorehabilitation studies.

DOI： 10.3389/fnhum.2022.805452

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OBJECTIVE: Recent studies have used sparse classifications to predict categorical variables from high-dimensional brain activity signals to expose human's mental states and intentions, selecting the relevant features automatically in the model training process. However, existing sparse classification models will likely be prone to the performance degradation which is caused by the noise inherent in the brain recordings. To address this issue, we aim to propose a new robust and sparse classification algorithm in this study. METHODS: To this end, we introduce the correntropy learning framework into the automatic relevance determination based sparse classification model, proposing a new correntropy-based robust sparse logistic regression algorithm. To demonstrate the superior brain activity decoding performance of the proposed algorithm, we evaluate it on a synthetic dataset, an electroencephalogram (EEG) dataset, and a functional magnetic resonance imaging (fMRI) dataset. RESULTS: The extensive experimental results confirm that not only the proposed method can achieve higher classification accuracy in a noisy and high-dimensional classification task, but also it would select those more informative features for the decoding tasks. CONCLUSION: Integrating the correntropy learning approach with the automatic relevance determination technique will significantly improve the robustness with respect to the noise, leading to more adequate robust sparse brain decoding algorithm. SIGNIFICANCE: It provides a more powerful approach in the real-world brain activity decoding and the brain-computer interfaces.

DOI： 10.48550/arXiv.2207.09693

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1142/S0129065721030106

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A significant problem in brain-computer interface (BCI) research is decoding - obtaining required information from very weak noisy electroencephalograph signals and extracting considerable information from limited data. Traditional intention decoding methods, which obtain information from induced or spontaneous brain activity, have shortcomings in terms of performance, computational expense and usage burden. Here, a new methodology called prediction error decoding was used for motor imagery (MI) detection and compared with direct intention decoding. Galvanic vestibular stimulation (GVS) was used to induce subliminal sensory feedback between the forehead and mastoids without any burden. Prediction errors were generated between the GVS-induced sensory feedback and the MI direction. The corresponding prediction error decoding of the front/back MI task was validated. A test decoding accuracy of 77.83-78.86% (median) was achieved during GVS for every 100[Formula: see text]ms interval. A nonzero weight parameter-based channel screening (WPS) method was proposed to select channels individually and commonly during GVS. When the WPS common-selected mode was compared with the WPS individual-selected mode and a classical channel selection method based on correlation coefficients (CCS), a satisfactory decoding performance of the selected channels was observed. The results indicated the positive impact of measuring common specific channels of the BCI.

DOI： 10.1142/S0129065721500349

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Optimal feedback control is an established framework that is used to characterize human movement. However, it is not fully understood how the brain computes optimal gains through interactions with the environment. In the past study, we proposed a model of motor learning that identifies a set of feedback and feedforward controllers and a state predictor of the arm musculoskeletal system to control free reaching movements. In this study, we applied the model to force field adaptation tasks where normal reaching movements are disturbed by an external force imposed on the hand. Without a priori knowledge about the arm and environment, the model was able to adapt to the force field by generating counteracting forces to overcome it in a manner similar to what is reported in the behavioral literature. The kinematics of the movements generated by our model share characteristic features of human movements observed before and after force field adaptation. In addition, we demonstrate that the structure and learning algorithm introduced in our model induced a shift in the end-point's equilibrium position and a static force modulation, accompanied by a fast and a slow learning process. Importantly, our model does not require desired trajectories, yields movements without specifying movement duration, and predicts force generation patterns by exploring the environment. Our model demonstrates a possible mechanism through which the central nervous system may control and adapt a point-to-point reaching movement without specifying a desired trajectory by continuously updating the body's musculoskeletal model.

DOI： 10.1016/j.neunet.2021.01.030

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The minimum error entropy (MEE) criterion is a powerful approach for non-Gaussian signal processing and robust machine learning. However, the instantiation of MEE on robust classification is a rather vacancy in the literature. The original MEE purely focuses on minimizing Renyi's quadratic entropy of the prediction errors, which could exhibit inferior capability in noisy classification tasks. To this end, we analyze the optimal error distribution with adverse outliers and introduce a specific codebook for restriction, which optimizes the error distribution toward the optimal case. Half-quadratic-based optimization and convergence analysis of the proposed learning criterion, called restricted MEE (RMEE), are provided. The experimental results considering logistic regression and extreme learning machine on synthetic data and UCI datasets, respectively, are presented to demonstrate the superior robustness of RMEE. Furthermore, we evaluate RMEE on a noisy electroencephalogram dataset, so as to strengthen its practical impact.

DOI： 10.1109/TNNLS.2021.3082571

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Publishing type：Research paper (scientific journal)   Publisher：Informa UK Limited  

DOI： 10.1080/01691864.2021.1900913

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In this article we present the study of electroencephalography (EEG) traits for emotion recognition process using a videogame as a stimuli tool, and considering two different kind of information related to emotions: arousal-valence self-assesses answers from participants, and game events that represented positive and negative emotional experiences under the videogame context. We performed a statistical analysis using Spearman's correlation between the EEG traits and the emotional information. We found that EEG traits had strong correlation with arousal and valence scores; also, common EEG traits with strong correlations, belonged to the theta band of the central channels. Then, we implemented a regression algorithm with feature selection to predict arousal and valence scores using EEG traits. We achieved better result for arousal regression, than for valence regression. EEG traits selected for arousal and valence regression belonged to time domain (standard deviation, complexity, mobility, kurtosis, skewness), and frequency domain (power spectral density-PDS, and differential entropy-DE from theta, alpha, beta, gamma, and all EEG frequency spectrum). Addressing game events, we found that EEG traits related with the theta, alpha and beta band had strong correlations. In addition, distinctive event-related potentials where identified in the presence of both types of game events. Finally, we implemented a classification algorithm to discriminate between positive and negative events using EEG traits to identify emotional information. We obtained good classification performance using only two traits related with frequency domain on the theta band and on the full EEG spectrum.

DOI： 10.3390/brainsci11030378

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Publishing type：Research paper (scientific journal)   Publisher：Wiley  

DOI： 10.1002/aisy.202000164

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Improving ski-turn skills is of interest to both competitive and recreational skiers, but it is not easy to improve on one's own. Although studies have reported various methods of ski-turn skill evaluation, a simple method that can be used by oneself has not yet been established. In this study, we have proposed a comfortable method to assess ski-turn skills; this method enables skiers to easily understand the relationship between body control and ski motion. One expert skier and four intermediate skiers participated in this study. Small inertial measurement units (IMUs) and mobile plantar pressure distribution sensors were used to capture data while skiing, and three ski-turn features-ski motion, waist rotation, and how load is applied to the skis-as well as their symmetry, were assessed. The results showed that the motions of skiing and the waist in the expert skier were significantly larger than those in intermediate skiers. Additionally, we found that the expert skier only slightly used the heel to apply a load to the skis (heel load ratio: approximately 60%) and made more symmetrical turns than the intermediate skiers did. This study will provide a method for recreational skiers, in particular, to conveniently and quantitatively evaluate their ski-turn skills by themselves.

DOI： 10.3390/s21030834

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Studying brain function is a challenging task. In the past, we could only study brain anatomical structures post-mortem, or infer brain functions from clinical data of patients with a brain injury. Nowadays technology, such as functional magnetic resonance imaging (fMRI), enable non-invasive brain activity observation. Several approaches have been proposed to interpret brain activity data. The brain connectivity model is a graphical tool that represents the interaction between brain regions, during certain states. It depicts how a brain region cause changes to other parts of the brain, which can be implied as information flow. This model can be used to help interpret how the brain works. There are several mathematical frameworks that can be used to infer the connectivity model from brain activity signals. Granger causality is one such approach and is one of the first that has been applied to brain activity data. However, due to the concept of the framework, such as the use of pairwise correlation, combined with the limitation of brain activity data such as low temporal resolution in case of fMRI signal, makes the interpretation of the connectivity difficult. We therefore propose the application of the Tigramite causal discovery framework on fMRI data. The Tigramite framework uses measures such as causal effect to analyze causal relations in the system. This enables the framework to identify both direct and indirect pathways or connectivities. In this paper, we applied the framework to the Human Connectome Project motor task-fMRI dataset. We then present the results and discuss how the framework improves interpretability of the connectivity model. We hope that this framework will help us understand more complex brain functions such as memory, consciousness, or the resting-state of the brain, in the future.

DOI： 10.3389/fninf.2021.619557

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To improve the life quality of forearm amputees, prosthetic hands with high accuracy, and robustness are necessary. The application of surface electromyography (sEMG) signals to control a prosthetic hand is challenging. In this study, we proposed a time-domain CNN model for the regression prediction of joint angles in three degrees of freedom (3-DOFs, include two wrist joint motion and one finger joint motion), and five-fold cross validation was used to evaluate the correlation coefficient (CC). The CC value results of wrist flexion/extension motion obtained from 10 participants was 0.87-0.92, pronation/supination motion was 0.72-0.95, and hand grip/open motion was 0.75-0.94. We backtracked the fully connected layer weights to create a geometry plot for analyzing the motion pattern to investigate the learning of the proposed model. In order to discuss the daily updateability of the model by transfer learning, we performed a second experiment on five of the participants in another day and conducted transfer learning based on smaller amount of dataset. The CC results improved (wrist flexion/extension was 0.90-0.97, pronation/supination was 0.84-0.96, hand grip/open was 0.85-0.92), suggesting the effectiveness of the transfer learning by incorporating the small amounts of sEMG data acquired in different days. We compared our CNN-based model with four conventional regression models, the result illustrates that proposed model significantly outperforms the four conventional models with and without transfer learning. The offline result suggests the reliability of the proposed model in real-time control in different days, it can be applied for real-time prosthetic control in the future.

DOI： 10.3389/fnbot.2021.685961

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Other Link： https://dblp.uni-trier.de/db/journals/corr/corr2106.html#abs-2106-13086

Language：English   Publishing type：Research paper (scientific journal)  

In various experimental settings, electromyography (EMG) signals have been used to control robots. EMG-based robot control requires intrinsic parameters for control, which makes it difficult for users to understand the input protocol. When a proper input is not provided, the response time of the system varies; as such, the user's subjective delay should be investigated regardless of the actual delay. In this study, we investigated the influence of the subjective perception of delay on brain activation. Brain recordings were taken while subjects used EMG signals to control a robot hand, which requires a basic processing delay. We used muscle synergy for the grip command of the robot hand. After controlling the robot by grasping their hand, one of four additional delay durations (0 ms, 50 ms, 125 ms, and 250 ms) was applied in every trial, and subjects were instructed to answer whether the delay was natural, additional, or whether they were not sure. We compared brain activity based on responses ("sure" and "not sure"). Our results revealed a significant power difference in the theta band of the parietal lobe, and this time range included the interval in which the subjects could not feel the delay. Our study provides important insights that should be considered when constructing an adaptive system and evaluating its usability.

DOI： 10.3389/fnsys.2021.767477

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To develop a more reliable brain-computer interface (BCI) for patients in the completely locked-in state (CLIS), here we propose a Pavlovian conditioning paradigm using galvanic vestibular stimulation (GVS), which can induce a strong sensation of equilibrium distortion in individuals. We hypothesized that associating two different sensations caused by two-directional GVS with the thoughts of "yes" and "no" by individuals would enable us to emphasize the differences in brain activity associated with the thoughts of yes and no and hence help us better distinguish the two from electroencephalography (EEG). We tested this hypothesis with 11 healthy and 1 CLIS participant. Our results showed that, first, conditioning of GVS with the thoughts of yes and no is possible. And second, the classification of whether an individual is thinking "yes" or "no" is significantly improved after the conditioning, even in the absence of subsequent GVS stimulations. We observed average classification accuracy of 73.0% over 11 healthy individuals and 85.3% with the CLIS patient. These results suggest the establishment of GVS-based Pavlovian conditioning and its usability as a noninvasive BCI.

DOI： 10.1093/texcom/tgab046

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DOI： 10.1002/aisy.202170023

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Humans have the ability to use a diverse range of handheld tools. Owing to its versatility, a virtual environment with haptic feedback of the force is ideally suited to investigating motor learning during tool use. However, few simulators exist to recreate the dynamic interactions during real tool use, and no study has compared the correlates of motor learning between a real and virtual tooling task. To this end, we compared two groups of participants who either learned to insert a real or virtual tool into a fixture. The trial duration, the movement speed, the force impulse after insertion and the endpoint stiffness magnitude decreased as a function of trials, but they changed at comparable rates in both environments. A ballistic insertion strategy observed in both environments suggests some interdependence when controlling motion and controlling interaction, contradicting a prominent theory of these two control modalities being independent of one another. Our results suggest that the brain learns real and virtual insertion in a comparable manner, thereby supporting the use of a virtual tooling task with haptic feedback to investigate motor learning during tool use.

DOI： 10.1038/s41598-020-79433-5

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The movement in a joint is facilitated by a pair of muscles that pull in opposite directions. The difference in the pair's muscle force or reciprocal activity results in joint torque, while the overlapping muscle force or the cocontraction is related to the joint's stiffness. Cocontraction knowingly adapts implicitly over a number of movements, but it is unclear whether the central nervous system can actively regulate cocontraction in a goal-directed manner in a short span of time. We developed a muscle interface where a cursor's horizontal position was determined by the reciprocal activity of the shoulder flexion-extension muscle pair, while the vertical position was controlled by its cocontraction. Participants made goal-directed movements to single and via-point targets in the two-dimensional muscle space, learning to move the cursor along the shortest path. Simulations using an optimal control framework suggest that the reciprocal activity and the cocontraction may be controlled independently by the CNS, albeit at a rate orders of magnitude slower than the muscle's maximal activation speed.

DOI： 10.1038/s41598-020-79526-1

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DOI： 10.3390/brainsci10120966

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DOI： 10.1038/s41598-019-57267-0

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The generation of walking patterns is central to bio-inspired robotics and has been attained using methods encompassing diverse numerical as well as analog implementations. Here, we demonstrate the possibility of synthesizing viable gaits using a paradigmatic low-dimensional non-linear entity, namely, the Rössler system, as a dynamical unit. Through a minimalistic network wherein each instance is univocally associated with one leg, it is possible to readily reproduce the canonical gaits as well as generate new ones via changing the coupling scheme and the associated delays. Varying levels of irregularity can be introduced by rendering individual systems or the entire network chaotic. Moreover, through tailored mapping of the state variables to physical angles, adequate leg trajectories can be accessed directly from the coupled systems. The functionality of the resulting generator was confirmed in laboratory experiments by means of an instrumented six-legged ant-like robot. Owing to their simple form, the 18 coupled equations could be rapidly integrated on a bare-metal microcontroller, leading to the demonstration of real-time robot control navigating an arena using a brain-machine interface.

DOI： 10.1063/5.0021694

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This Letter provides a low-power method for chaos generation that is generally applicable to nonlinear micro- and nanoelectromechanical systems (MNEMS) resonators. The approach taken is independent of the material, scale, design, and actuation of the device in question; it simply assumes a good quality factor and a Duffing type nonlinearity, features that are commonplace to MNEMS resonators. The approach models the rotating-frame dynamics to analytically constrain the parameter space required for chaos generation. By leveraging these common properties of MNEMS devices, a period-doubling route to chaos is generated using smaller forcing than typically reported in the literature.

DOI： 10.1103/PhysRevLett.125.174301

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Among brain-computer interface studies, electroencephalography (EEG)-based emotion recognition is receiving attention and some studies have performed regression analyses to recognize small-scale emotional changes; however, effective brain regions in emotion regression analyses have not been identified yet. Accordingly, this study sought to identify neural activities correlating with emotional states in the source space. We employed independent component analysis, followed by a source localization method, to obtain distinct neural activities from EEG signals. After the identification of seven independent component (IC) clusters in a k-means clustering analysis, group-level regression analyses using frequency band power of the ICs were performed based on Russell's valence-arousal model. As a result, in the regression of the valence level, an IC cluster located in the cuneus predicted both high- and low-valence states and two other IC clusters located in the left precentral gyrus and the precuneus predicted the low-valence state. In the regression of the arousal level, the IC cluster located in the cuneus predicted both high- and low-arousal states and two posterior IC clusters located in the cingulate gyrus and the precuneus predicted the high-arousal state. In this proof-of-concept study, we revealed neural activities correlating with specific emotional states across participants, despite individual differences in emotional processing.

DOI： 10.3390/brainsci10100669

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Muscle synergies are usually identified via dimensionality reduction techniques, such that the identified synergies reconstruct the muscle activity to an accuracy level defined heuristically, often set to 90% of the variance. Here, we question the assumption that the residual muscle activity not explained by the synergies is due to noise. We hypothesize instead that the residual activity is not entirely random and can influence the execution of motor tasks. Young healthy subjects performed an isometric reaching task in which the surface electromyography of 10 arm muscles was mapped onto a two-dimensional force used to control a cursor. Three to five synergies explained 90% of the variance in muscle activity. We altered the muscle-force mapping via "hard" and "easy" virtual surgeries. Whereas in both surgeries the forces associated with synergies spanned the same dimension of the virtual environment, the muscle-force mapping was as close as possible to the initial mapping in the easy surgery; in contrast, it was as far as possible in the hard surgery. This design maximized potential differences in reaching errors attributable to residual activity. Results show that the easy surgery produced smaller directional errors than the hard surgery. Additionally, simulations of surgeries constructed with 1 to 10 synergies show that the errors in the easy and hard surgeries differ significantly for up to 8 synergies, which explains 98% of the variance on average. Our study thus indicates the need for cautious interpretations of results derived from synergy extraction techniques based on heuristics with lenient accuracy levels.NEW & NOTEWORTHY The muscle synergy hypothesis posits that the central nervous system simplifies motor control by grouping muscles into modules. Current techniques use dimensionality reduction, such that the identified synergies reconstruct 90% of the muscle activity. We show that residual muscle activity following such identification can have a large systematic effect on movements, even when the number of synergies approaches the number of muscles. Current synergy extraction techniques must therefore be updated to identify true physiological synergies.

DOI： 10.1152/jn.00472.2019

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Over the past two decades, the number of studies on work engagement has increased rapidly. Work engagement refers to a positive, affective-motivational state of high energy combined with high levels of dedication and a strong focus on work, leading to various work-related outcomes, including higher work performance. Several studies have indicated that training or coaching may increase work engagement, but other studies have shown contradicting results. These inconsistencies may be due to the indirectness between training/coaching and work engagement. Therefore, we investigated the relationship between training and brain structure as well as between brain structure and work engagement in cognitively normal participants. Brain structure was assessed using neuroimaging-derived measures, including the gray-matter brain healthcare quotient (GM-BHQ) and the fractional-anisotropy brain healthcare quotient (FA-BHQ), which are approved as the international standard (H.861.1) by ITU-T. Work engagement was assessed using the Utrecht Work Engagement Scale. To validate and enrich the analysis, we employed another two representative questionnaires, which are known to be close to but different from work engagement: The Social interaction Anxiety Scale and the Maslach Burnout Inventory-General Survey to gauge the levels of human relation ineffectiveness and burnout. The latter scale is subdivided into three variables including "Exhaustion," "Cynicism," and "Professional Efficacy." The results of the present study indicate that training is associated with an increase of FA-BHQ scores, and that an increase of the FA-BHQ scores is associated with an increase in Work Engagement and a decrease in Cynicism. On the other hand, the training with coaching was associated with a decrease in Interaction Anxiety. However, no correlation was observed for training with Work Engagement or the subscales of Burnout. Likewise, no correlation was observed for FA-BHQ with Exhaustion, Professional Efficacy, and Interaction Anxiety. The results of the current research provide the possibility to use brain information to evaluate training effectiveness from the viewpoint of neuroscience.

DOI： 10.1038/s41598-020-63711-3

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Publishing type：Research paper (scientific journal)   Publisher：MDPI AG  

We analyzed the contribution of electroencephalogram (EEG) data, age, sex, and personality traits to emotion recognition processes—through the classification of arousal, valence, and discrete emotions labels—using feature selection techniques and machine learning classifiers. EEG traits and age, sex, and personality traits were retrieved from a well-known dataset—AMIGOS—and two sets of traits were built to analyze the classification performance. We found that age, sex, and personality traits were not significantly associated with the classification of arousal, valence and discrete emotions using machine learning. The added EEG features increased the classification accuracies (compared with the original report), for arousal and valence labels. Classification of arousal and valence labels achieved higher than chance levels; however, they did not exceed 70% accuracy in the different tested scenarios. For discrete emotions, the mean accuracies and the mean area under the curve scores were higher than chance; however, F1 scores were low, implying that several false positives and false negatives were present. This study highlights the performance of EEG traits, age, sex, and personality traits using emotion classifiers. These findings could help to understand the traits relationship in a technological and data level for personalized human-computer interactions systems.

DOI： 10.3390/make2020007

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Publishing type：Research paper (scientific journal)   Publisher：MYU K.K.  

DOI： 10.18494/sam.2020.2636

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Institute of Electrical and Electronics Engineers Inc.  

DOI： 10.1109/SAMI48414.2020.9108746

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In this study, seven-channel electromyography signal-based two-dimensional wrist joint movement estimation with and without handgrip motions was carried out. Electromyography signals were analyzed using the synergy-based linear regression model and musculoskeletal model; they were subsequently compared with respect to single and combined wrist joint movements and handgrip. Using each one of wrist motion and grip trial as a training set, the synergy-based linear regression model exhibited a statistically significant performance with 0.7891 ± 0.0844 Pearson correlation coefficient (r) value in two-dimensional wrist motion estimation compared with 0.7608 ±  0.1037 r value of the musculoskeletal model. Estimates on the grip force produced 0.8463 ± 0.0503 r value with 0.2559 ± 0.1397 normalized root-mean-square error of the wrist motion range. This continuous wrist and handgrip estimation can be considered when electromyography-based multi-dimensional input signals in the prosthesis, virtual interface, and rehabilitation are needed.

DOI： 10.1155/2020/5451219

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/SMC42975.2020.9283321

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Other Link： https://dblp.uni-trier.de/db/conf/smc/smc2020.html#Martinez-Tejada20

Publishing type：Research paper (scientific journal)  

DOI： 10.18494/SAM.2020.2636

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/SMC42975.2020.9283342

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Other Link： https://dblp.uni-trier.de/db/conf/smc/smc2020.html#KimYK20

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Surface electromyography (EMG) measurements are affected by various noises such as power source and movement artifacts and adjacent muscle activities. Hardware solutions have been found that use multi-channel EMG signal to attenuate noise signals related to sensor positions. However, studies addressing the overcoming of crosstalk from EMG and the division of overlaid superficial and deep muscles are scarce. In this study, two signal decompositions-independent component analysis and non-negative matrix factorization-were used to create a low-dimensional input signal that divides noise, surface muscles, and deep muscles and utilizes them for movement classification based on direction. In the case of index finger movement, it was confirmed that the proposed decomposition method improved the classification performance with the least input dimensions. These results suggest a new method to analyze more dexterous movements of the hand by separating superficial and deep muscles in the future using multi-channel EMG signals.

DOI： 10.3389/fnins.2020.600804

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Humans can innately track a moving target by anticipating its future position from a brief history of observations. While ballistic trajectories can be readily extrapolated, many natural and artificial systems are governed by more general nonlinear dynamics and, therefore, can produce highly irregular motion. Yet, relatively little is known regarding the behavioral and physiological underpinnings of prediction and tracking in the presence of chaos. Here, we investigated in lab settings whether participants could manually follow the orbit of a paradigmatic chaotic system, the Rössler equations, on the (x,y) plane under different settings of a control parameter, which determined the prominence of transients in the target position. Tracking accuracy was negatively related to the level of unpredictability and folding. Nevertheless, while participants initially reacted to the transients, they gradually learned to anticipate it. This was accompanied by a decrease in muscular co-contraction, alongside enhanced activity in the theta and beta EEG bands for the highest levels of chaoticity. Furthermore, greater phase synchronization of breathing was observed. Taken together, these findings point to the possible ability of the nervous system to implicitly learn topological regularities even in the context of highly irregular motion, reflecting in multiple observables at the physiological level.

DOI： 10.1371/journal.pone.0239471

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Error-related brain activation has been investigated for advanced brain-machine interfaces (BMI). However, how a delayed response of cursor control in BMI systems should be handled is not clear. Therefore, the purpose of this study was to investigate how participants responded to delayed cursor control. Six subjects participated in the experiment and performed a wrist-bending task. For three distinct delay intervals (an interval where participants could not perceive the delay, an interval where participants could not be sure whether there was a delay or not, and an interval where participants could perceive the delay), we assessed two types of binary classifications ("Yes + No" vs. "I don't know" and "Yes" vs. "No") based on participants' responses and applied delay times (thus, four types of classification, overall). For most participants, the "Yes vs. No" classification had higher accuracy than "Yes + No" vs. "I don't know" classification. For the "Yes + No" vs. "I don't know" classification, most participants displayed higher accuracy based on response classification than delay classification. Our results demonstrate that a class only for "I don't know" largely contributed to these differences. Many independent components (ICs) that exhibited high accuracy in "Yes + No" vs. "I don't know" response classification were associated with activation of areas from the frontal to parietal lobes, while many ICs that showed high accuracy in the "Yes vs. No" classification were associated with activation of an area ranging from the parietal to the occipital lobes and were more broadly localized in cortical regions than was seen for the "Yes + No" vs. "I don't know" classification. Our results suggest that small and large delays in real-time cursor control differ not only in the magnitude of the delay but should be handled as distinct information in different ways and might involve differential processing in the brain.

DOI： 10.1155/2020/1418437

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Publishing type：Research paper (scientific journal)   Publisher：Institute of Electrical and Electronics Engineers ({IEEE})  

DOI： 10.1109/ACCESS.2020.2976139

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Institute of Physics Publishing  

DOI： 10.1088/1742-6596/1379/1/012058

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Society for Neuroscience  

DOI： 10.1523/ENEURO.0095-19.2019

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The entrainment between weakly coupled nonlinear oscillators, as well as between complex signals such as those representing physiological activity, is frequently assessed in terms of whether a stable relationship is detectable between the instantaneous phases extracted from the measured or simulated time-series via the analytic signal. Here, we demonstrate that adding a possibly complex constant value to this normally null-mean signal has a non-trivial warping effect. Among other consequences, this introduces a level of sensitivity to the amplitude fluctuations and average relative phase. By means of simulations of Rössler systems and experiments on single-transistor oscillator networks, it is shown that the resulting coherence measure may have an empirical value in improving the inference of the structural couplings from the dynamics. When tentatively applied to the electroencephalogram recorded while performing imaginary and real movements, this straightforward modification of the phase locking value substantially improved the classification accuracy. Hence, its possible practical relevance in brain-computer and brain-machine interfaces deserves consideration.

DOI： 10.1063/1.5082749

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Institute of Electrical and Electronics Engineers Inc.  

DOI： 10.1109/SMC.2018.00031

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Publishing type：Research paper (scientific journal)   Publisher：Institute of Electrical and Electronics Engineers ({IEEE})  

DOI： 10.1109/ACCESS.2019.2957014

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DOI： 10.1109/ACCESS.2019.2912903

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Other Link： https://dblp.uni-trier.de/db/journals/corr/corr1909.html#abs-1909-02707

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Undesirable habitual or addictive behaviors are often difficult to change. The issue of "behavior change" has long been studied in various research fields. Several models for behavior change have converged to the hypothesis that attitudes, norms, and self-efficacy are important determinants of intentions and behavior. To improve the accuracy of behavior-change models, some researchers have tried to combine behavioral economics models with existing models for behavior change. However, these attempts have failed because the existing models [e.g., Theory of Planned Behavior (TPB)] are not consistent with Expected Utility Theory (EUT), which underlies various behavioral economics models. In the present paper, we clarify the corresponding components between existing models for behavior change and EUT, and propose a new model, the Decision-Theoretic Model of behavior change (DTM), which is a natural extension of ordinary EUT.

DOI： 10.3389/fpsyg.2019.01042

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Many previous studies on brain-machine interfaces (BMIs) have focused on electroencephalography (EEG) signals elicited during motor-command execution to generate device commands. However, exploiting pre-execution brain activity related to movement intention could improve the practical applicability of BMIs. Therefore, in this study we investigated whether EEG signals occurring before movement execution could be used to classify movement intention. Six subjects performed reaching tasks that required them to move a cursor to one of four targets distributed horizontally and vertically from the center. Using independent components of EEG acquired during a premovement phase, two-class classifications were performed for left vs. right trials and top vs. bottom trials using a support vector machine. Instructions were presented visually (test) and aurally (condition). In the test condition, accuracy for a single window was about 75%, and it increased to 85% in classification using two windows. In the control condition, accuracy for a single window was about 73%, and it increased to 80% in classification using two windows. Classification results showed that a combination of two windows from different time intervals during the premovement phase improved classification performance in the both conditions compared to a single window classification. By categorizing the independent components according to spatial pattern, we found that information depending on the modality can improve classification performance. We confirmed that EEG signals occurring during movement preparation can be used to control a BMI.

DOI： 10.3389/fnhum.2019.00063

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The utility of premovement electroencephalography (EEG) for decoding movement intention during a reaching task has been demonstrated. However, the kind of information the brain represents regarding the intended target during movement preparation remains unknown. In the present study, we investigated which movement parameters (i.e., direction, distance, and positions for reaching) can be decoded in premovement EEG decoding. Eight participants performed 30 types of reaching movements that consisted of 1 of 24 movement directions, 7 movement distances, 5 horizontal target positions, and 5 vertical target positions. Event-related spectral perturbations were extracted using independent components, some of which were selected via an analysis of variance for further binary classification analysis using a support vector machine. When each parameter was used for class labeling, all possible binary classifications were performed. Classification accuracies for direction and distance were significantly higher than chance level, although no significant differences were observed for position. For the classification in which each movement was considered as a different class, the parameters comprising two vectors representing each movement were analyzed. In this case, classification accuracies were high when differences in distance were high, the sum of distances was high, angular differences were large, and differences in the target positions were high. The findings further revealed that direction and distance may provide the largest contributions to movement. In addition, regardless of the parameter, useful features for classification are easily found over the parietal and occipital areas.

DOI： 10.3389/fnins.2019.01148

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Surface ElectroMyoGraphy (EMG) signals from the forearm used in prosthetic hand and finger control systems require precise anatomy data of finger muscles that are small and located deep within the forearm. The main problem of this method is that the signal quality depends on the placement of EMG sensor, which can significantly affects the accuracy and precision to estimate joint angles or forces. Moreover, in case of amputees, the location of finger muscles is unknown and needed to be identified manually for EMG recording. As a result, most modern prosthetic hands utilize limited number of muscles with pattern recognition to control finger according to pre-defined grip which is unable to mimic natural finger motion. To address such issue, we used array EMG sensors to obtain EMG signals from all possible positions on the forearm and applied regression method to produce natural finger motion. The signals were analyzed using independent component analysis (ICA) to find the best-fitted independent component (IC) that matches the anatomical data taken after the experiment. Next, from the IC and EMG signals, finger angles were estimated using linear regression model (LRM). Each finger was assigned EMG and IC component for flexion and extension muscles, to assess the possibility of controlling each finger angle separately. We compared the joint angles of each finger between calculated from IC and EMG by correlation coefficients (CC) for all fingers. The average CC values were higher than 0.7, demonstrating the strength of the linear relationship. The different between IC and EMG methods suggests that the IC method can reduce noise and increase the signal to noise ratio. The performance of ICA method showed higher CC value at around 0.2 ± 0.10. In order to confirm the performance of ICA method, we also tested mathematical musculoskeletal model (MSM). The result from this study showed that not only array EMG sensors with ICA significantly improve the quality of signal detected from forearm but also reduce problems of conventional EMG sensors and consequently improve the performance of regression method to imitate natural finger motion.

DOI： 10.3389/fnbot.2019.00075

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Other Link： https://dblp.uni-trier.de/db/journals/finr/finr13.html#StapornchaisitK19

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In both neurorehabilitation and functional augmentation, the patient or the user's muscular effort diminishes when the movement of their limb is supported by a robot. Is this relaxation a result of "slacking" by letting the robot take-over the movement, resulting in less responsiveness in the task? To address this question, we tested subjects who controlled a virtual cursor isometrically to track a moving target without and with different assistants. We measured the force applied by the subject as a metric for effort and estimated their control gain as the metric for responsiveness in the task. Although subjects applied less force with position assistance, the norm of the control gain increased with all assistants, i.e., they applied proportionately larger forces for the same difference between the cursor and the target states. Furthermore, assisting velocity errors improved baseline performance without reducing effort. Though all assistants improved task performance, the control gain adapted differently to position and velocity assistance. Position assistance was exploited to accurately track the target, whereas velocity assistance was treated as a disturbance, and was effectively nullified as it prevented submovements that minimized positional error. Our results show that robotic assistance increases task responsiveness in healthy individuals and that assisting velocity errors could boost patient performance without reducing their motor effort.

DOI： 10.1109/TNSRE.2018.2836341

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：American Association for the Advancement of Science ({AAAS})  

We propose a new methodology for decoding movement intentions of humans. This methodology is motivated by the well-documented ability of the brain to predict sensory outcomes of self-generated and imagined actions using so-called forward models. We propose to subliminally stimulate the sensory modality corresponding to a user's intended movement, and decode a user's movement intention from his electroencephalography (EEG), by decoding for prediction errors-whether the sensory prediction corresponding to a user's intended movement matches the subliminal sensory stimulation we induce. We tested our proposal in a binary wheelchair turning task in which users thought of turning their wheelchair either left or right. We stimulated their vestibular system subliminally, toward either the left or the right direction, using a galvanic vestibular stimulator and show that the decoding for prediction errors from the EEG can radically improve movement intention decoding performance. We observed an 87.2% median single-trial decoding accuracy across tested participants, with zero user training, within 96 ms of the stimulation, and with no additional cognitive load on the users because the stimulation was subliminal.

DOI： 10.1126/sciadv.aaq0183

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Institute of Electrical and Electronics Engineers Inc.  

DOI： 10.1109/ACCESS.2018.2799145

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DOI： 10.3389/fnins.2017.00733

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Electrocorticogram (ECoG) is a well-known recording method for the less invasive brain machine interface (BMI). Our previous studies have succeeded in predicting muscle activities and arm trajectories from ECoG signals. Despite such successful studies, there still remain solving works for the purpose of realizing an ECoG-based prosthesis. We suggest a neuromuscular interface to control robot using decoded muscle activities and joint angles. We used sparse linear regression to find the best fit between band-passed ECoGs and electromyograms (EMG) or joint angles. The best coefficient of determination for 100 s continuous prediction was 0.6333 ± 0.0033 (muscle activations) and 0.6359 ± 0.0929 (joint angles), respectively. We also controlled a 4 degree of freedom (DOF) robot arm using only decoded 4 DOF angles from the ECoGs in this study. Consequently, this study shows the possibility of contributing to future advancements in neuroprosthesis and neurorehabilitation technology.

DOI： 10.1155/2018/2580165

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Other Link： https://www.hindawi.com/journals/cin/2018/2580165/cta/

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Although classical decision-making studies have assumed that subjects behave in a Bayes-optimal way, the sub-optimality that causes biases in decision-making is currently under debate. Here, we propose a synthesis based on exponentially-biased Bayesian inference, including various decision-making and probability judgments with different bias levels. We arrange three major parameter estimation methods in a two-dimensional bias parameter space (prior and likelihood), of the biased Bayesian inference. Then, we discuss a neural implementation of the biased Bayesian inference on the basis of changes in weights in neural connections, which we regarded as a combination of leaky/unstable neural integrator and probabilistic population coding. Finally, we discuss mechanisms of cognitive control which may regulate the bias levels.

DOI： 10.3389/fnins.2018.00734

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DOI： 10.1038/s41598-017-09770-5

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DOI： 10.1038/srep45486

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DOI： 10.1088/1741-2552/aa525f

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Frontiers Media S.A.  

DOI： 10.3389/fnins.2017.00733

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DOI： 10.1080/01691864.2016.1215935

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Language：Japanese   Publisher：日本公衆衛生学会  

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DOI： 10.1016/j.dib.2016.06.054

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DOI： 10.1016/j.neulet.2016.05.044

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DOI： 10.3389/fnins.2016.00175

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DOI： 10.1038/srep25432

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DOI： 10.3389/fnins.2016.00175

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DOI： 10.1109/ACCESS.2017.2647851

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DOI： 10.1007/s00146-014-0559-2

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DOI： 10.1155/2015/653639

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier Ltd  

DOI： 10.1016/j.bspc.2015.01.006

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Other Link： https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-26540104/

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DOI： 10.1155/2015/653639

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DOI： 10.1016/j.bspc.2014.10.005

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DOI： 10.3389/fpsyg.2015.00892

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Language：Japanese   Publisher：公益社団法人 計測自動制御学会  

DOI： 10.9746/sicetr.51.519

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IMEKO-International Measurement Federation Secretariat  

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DOI： 10.1016/j.neuroimage.2014.04.046

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DOI： 10.1016/j.neures.2014.05.005

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DOI： 10.1016/j.neuroscience.2014.05.058

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DOI： 10.1016/j.neures.2014.03.010

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DOI： 10.5405/jmbe.1596

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Science and Engineering Research Support Society  

DOI： 10.14257/ijsia.2014.8.4.13

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DOI： 10.11477/mf.5001100135

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IMEKO-International Measurement Federation Secretariat  

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DOI： 10.1371/journal.pone.0083534

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DOI： 10.3389/fpsyg.2013.00040

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DOI： 10.1371/journal.pone.0072085

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DOI： 10.1152/jn.00542.2011

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DOI： 10.1016/j.robot.2011.06.011

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DOI： 10.1016/j.neuroimage.2011.08.029

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Fuji Technology Press  

DOI： 10.20965/jrm.2012.p0205

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DOI： 10.1371/journal.pone.0047992

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DOI： 10.1007/978-4-431-54008-3_3

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DOI： 10.1109/TNSRE.2010.2049373

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DOI： 10.1016/j.neures.2011.07.874

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DOI： 10.2316/P.2011.751-041

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DOI： 10.1109/ICVR.2011.5971870

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DOI： 10.20965/jaciii.2011.p0972

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DOI： 10.1123/jab.27.4.306

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DOI： 10.1109/TBME.2009.2039997

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DOI： 10.1109/IEMBS.2010.5626477

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DOI： 10.1016/j.neures.2010.07.1454

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DOI： 10.1016/j.neures.2009.01.009

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DOI： 10.1587/transinf.E92.D.705

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DOI： 10.1152/jn.00584.2007

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DOI： 10.1109/IJCNN.2009.5178818

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DOI： 10.1016/j.neunet.2008.11.004

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DOI： 10.1016/j.neunet.2009.05.008

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DOI： 10.1016/j.neunet.2009.09.003

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DOI： 10.1007/978-3-642-03889-1_116

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computing Machinery  

DOI： 10.1145/1592700.1592731

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DOI： 10.1109/BMEI.2009.5305646

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DOI： 10.1145/1592700.1592732

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DOI： 10.1007/978-3-540-69162-4_100

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DOI： 10.1109/ICCME.2007.4381946

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DOI： 10.1016/j.neures.2006.02.012

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DOI： 10.1111/j.1468-5884.2006.00316.x

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DOI： 10.1007/11941354_128

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DOI： 10.1109/IEMBS.2005.1615812

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Institute of Electrical and Electronics Engineers Inc.  

DOI： 10.1109/WHC.2005.5

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DOI： 10.1533/abbi.2004.0054

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DOI： 10.1109/IEMBS.2005.1615437

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DOI： 10.1002/ecjb.20117

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DOI： 10.1109/HAPTIC.2003.1191304

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer New York LLC  

DOI： 10.1007/978-0-387-35660-0

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DOI： 10.1162/1054746021470586

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DOI： 10.1007/s00422-001-0286-x

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Publisher：The Society of Instrument and Control Engineers  

Creating interactive motion with existing motion-capture systems is difficult because it requires a precise preparation of the real environment. In order to resolve such a drawback, we propose a new interactive motion capture system that combines the existing motion capture system with a haptic device and a human-scale virtual environment. As our system has built virtual environment with haptic elements, it is very useful for generating a natural-looking motion data like interaction in the real world.

DOI： 10.11499/siced.si2002.0.509.0

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DOI： 10.1007/s00422-001-0286-x

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Publisher：Kluwer  

DOI： 10.1007/978-0-387-35660-0

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computing Machinery  

DOI： 10.1145/502390.502428

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Language：English   Publisher：The Institute of Image Information and Television Engineers  

This paper describes the principle and experimental results of a camera system that is composed of a commercially available CCD camera, a largeaperture lens, and an intermediate screen used to measure the distance between the camera and a faraway object by using Depth From Focus.

DOI： 10.11485/itetr.24.2.0_123

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE Computer Society  

DOI： 10.1109/PADSW.2000.884552

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Other Link： https://dblp.uni-trier.de/db/conf/icpads/icpads2000w.html#HasegawaISK00

Publishing type：Research paper (international conference proceedings)   Publisher：IEEE Computer Society  

DOI： 10.1109/ICPR.2000.903024

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Other Link： https://dblp.uni-trier.de/db/conf/icpr/icpr2000-4.html#MiwaIKS00

Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEEE  

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Language：Japanese   Publisher：3次元画像コンファレンス実行委員会  

DOI： 10.60374/sanjigen.7.0_134

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DOI： 10.1016/S0893-6080(96)00043-3

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DOI： 10.1002/ecjc.4430790902

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DOI： 10.1007/BF00199052

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DOI： 10.1007/BF00199465

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DOI： 10.3758/BF03211693

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J-GLOBAL

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J-GLOBAL

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Language：English   Publisher：電子情報通信学会  

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CiNii Books

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J-GLOBAL

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J-GLOBAL

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Language：Japanese   Publisher：電子情報通信学会  

J-GLOBAL

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J-GLOBAL

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DOI： 10.1016/j.bspc.2015.01.006

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

When we play a score that have never seen, we execute sight reading playing necessarily. But, there are no effective exercise way or system supporting exercises in sight reading playing exercise. Recently, line of sight movement while playing the piano was studied, those have indicated that eye hand span is at least 2 notes. And we apply the result to our proposal system. To make our system, we record line of sight and performance while playing subject sight reading. The subjects are two, one of them is professional, and another is novice. As a result of analysis of recorded line of sight and performance, when professional can sight reading playing nicely keeping a indicated pace of performance, professional saw ahead of 2 notes. And, it is necessary that considering the pace of performance players set a time reading notes and a time making hands figure in a particular time. We make the practice system to novice learn that 2 points. Novice had practiced sight reading playing using proposal practice system during a month. After that performance of novice has changed.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

Brain-machine interface technology has a possibility to be applied to practical neuroprosthesis for disabled persons. For example, the trajectory decoding of the human elbow and wrist has been already achieved in various studies. However, the accurate prediction for trajectory of finger motion is still a challengeable problem. We subdurally implanted 90 electrocorticogram (ECoG) electrodes on the human sensorimotor cortex and predicted three-dimensional trajectories of the thumb, index and middle fingers base on the ECoG signals using a sparse linear regression. A participant put her right hand on a table palm-down, tapping the table with one of her thumb, index and middle fingers as tasks. The average of Pearson's correlation coefficient and normalized root-mean-square error between the predicted and actual trajectories were 0.83-0.90 and 0.24-0.48, respectively.

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Language：English   Publisher：The Institute of Electronics, Information and Communication Engineers  

This article describes a novel approach to biometric-based mobile security systems: a myokinetic password using surface EMG signals. Without modifying either password length or character variations, we observed the possibility where traditional 4-digits password systems can be enhanced. Here we present an implementation of an intuitive password system that takes advantage of muscle activation to enhance password complexity. We paired each password digit with a muscle activation level from EMG signals. This allows each individual digit to have multiple possible states, thereby increasing the number of password combinations. Furthermore, we have verified the scalability of this technique by increasing the support up to three muscle activation levels. By utilizing the proposed system, the number of possible input key combination is increased from 256 to 810,000 with three muscle levels. The password can also avoid being exploited by up to 80 key entries, given that the intruder knows all the digit combination.

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Language：English   Publisher：The Institute of Electronics, Information and Communication Engineers  

This article describes a novel approach to biometric-based mobile security systems: a myokinetic password using surface EMG signals. Without modifying either password length or character variations, we observed the possibility where traditional 4-digits password systems can be enhanced. Here we present an implementation of an intuitive password system that takes advantage of muscle activation to enhance password complexity. We paired each password digit with a muscle activation level from EMG signals. This allows each individual digit to have multiple possible states, thereby increasing the number of password combinations. Furthermore, we have verified the scalability of this technique by increasing the support up to three muscle activation levels. By utilizing the proposed system, the number of possible input key combination is increased from 256 to 810,000 with three muscle levels. The password can also avoid being exploited by up to 80 key entries, given that the intruder knows all the digit combination.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

When we play a score that have never seen, we execute sight reading playing necessarily. But, there are no effective exercise way or system supporting exercises in sight reading playing exercise. Recently, line of sight movement while playing the piano was studied, those have indicated that eye hand span is at least 2 notes. And we apply the result to our proposal system. To make our system, we record line of sight and performance while playing subject sight reading. The subjects are two, one of them is professional, and another is novice. As a result of analysis of recorded line of sight and performance, when professional can sight reading playing nicely keeping a indicated pace of performance, professional saw ahead of 2 notes. And, it is necessary that considering the pace of performance players set a time reading notes and a time making hands figure in a particular time. We make the practice system to novice learn that 2 points. Novice had practiced sight reading playing using proposal practice system during a month. After that performance of novice has changed.

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Language：English   Publisher：Springer Japan  

DOI： 10.1007/978-4-431-55037-2_4

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Language：Japanese   Publisher：一般社団法人電子情報通信学会  

This paper aims to reveal the mechanism how our brain adapts acceleration through sensory information when catching a falling ball. It is considered that human can modify estimated timing according to gaps between their expected and actual hand movements. To verify this hypothesis, we conducted ball-catching experiments in a virtual reality environment, which allowed participants to recognize the ball contact to the hand by visual information only. Our results indicated that three out of four participants could learn another acceleration other than the gravitational acceleration by visual information.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

Concerns about the usability of human-computer interfaces have been increasing in recent years. Although electroencephalography (EEG) is considered to be capable to evaluate the usability, previous researches using EEG mainly worked only on basic emotions, which are not sufficient to represent the affective state corresponding to usability. In this research, we aim to distinguish "Irritating" state, which can be considered to express the feeling of hardness of the task. We configured a novel method to elicit "Irritating" state using target reaching tasks on a computer. We simultaneously recorded EEG signals during the tasks and classified the affective state using Support Vector Machine (SVM). The accuracy of classification was reached over 84%.

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Language：Japanese   Publisher：一般社団法人電子情報通信学会  

Recently, It has been reported that classification the behavior and motor images, such as the wrist movements by using the brain activity. However, It had not been reported classified in the strength of the forces and finger movements from electroencephalogram (EEG). In this study, It was confirmed that it can be classified with finger flexion and extension, high force and low force using estimated cortex signal source current from EEG. Cortex signal source current was estimated at primary motor cortex, premotor cortex, supplementary motor area by using the brain data of EEG and nuclear Magnetic Resonance Imaging (MRI), Variational Bayesian Multimodal Encephalography (VBMEG). Furthermore, it was confirmed that high classification rate is achieved by using the frequency spectrum of the waveform since the firing rate of neurons varies with movements.

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Language：Japanese   Publisher：一般社団法人電子情報通信学会  

Concerns about the usability of human-computer interfaces have been increasing in recent years. Although electroencephalography (EEG) is considered to be capable to evaluate the usability, previous researches using EEG mainly worked only on basic emotions, which are not sufficient to represent the affective state corresponding to usability. In this research, we aim to distinguish "Irritating" state, which can be considered to express the feeling of hardness of the task. We configured a novel method to elicit "Irritating" state using target reaching tasks on a computer. We simultaneously recorded EEG signals during the tasks and classified the affective state using Support Vector Machine (SVM). The accuracy of classification was reached over 84%.

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Language：English  

J-GLOBAL

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J-GLOBAL

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Language：Japanese   Publisher：一般社団法人電子情報通信学会  

We perform reaching movement in daily life. Hand paths during reaching movements become almost straight and the speed profiles become bell-shaped. Even if some external force is applied to the hand, the trajectories become almost same as the ones during movements without any external force. Recently, the motor control and learning model combining the reinforcement learning and feedback error learning was proposed. In this research, to evaluate the validity of the model, we executed the motor control and learning simulation and compare results with the characteristics observed in human's movements. As the result, hand path and speed profile show the human characteristic. However, no coactivation was observed in the simulation. It suggest that this model need some mechanism which makes coactivation.

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Language：Japanese   Publisher：一般社団法人電子情報通信学会  

When we try to hold an object, we have to predict the weight of the object to generate counteracting force. If we are not certain about the prediction, however, we tend to stiffen our arm and try to avoid unexpected movement caused by wrong prediction. In this paper, we applied the optimal control theory to a ball-catching task to simulate how wrist impedance is adjusted against the degree of uncertainty in ball's weight. The result of the simulation matched with the result of the experiment of human subject and implies that our brain may be realizing optimal impedance under the trade-off between movement accuracy and effort.

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Language：Japanese   Publisher：一般社団法人情報処理学会  

When we try to hold an object, we have to predict the weight of the object to generate counteracting force. If we are not certain about the prediction, however, we tend to stiffen our arm and try to avoid unexpected movement caused by wrong prediction. In this paper, we applied the optimal control theory to a ball-catching task to simulate how wrist impedance is adjusted against the degree of uncertainty in ball' s weight. The result of the simulation matched with the result of the experiment of human subject and implies that our brain may be realizing optimal impedance under the trade-off between movement accuracy and effort.

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Language：Japanese   Publisher：一般社団法人情報処理学会  

We perform reaching movement in daily life. Hand paths during reaching movements become almost straight and the speed profiles become bell-shaped. Even if some external force is applied to the hand, the trajectories become almost same as the ones during movements without any external force. Recently, the motor control and learning model combining the reinforcement learning and feedback error learning was proposed. In this research, to evaluate the validity of the model, we executed the motor control and learning simulation and compare results with the characteristics observed in human's movements. As the result, hand path and speed profile show the human characteristic. However, no coactivation was observed in the simulation. It suggest that this model need some mechanism which makes coactivation.

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Language：Japanese   Publisher：一般社団法人情報処理学会  

We perform reaching movement in daily life. Hand paths during reaching movements become almost straight and the speed profiles become bell-shaped. Even if some external force is applied to the hand, the trajectories become almost same as the ones during movements without any external force. Recently, the motor control and learning model combining the reinforcement learning and feedback error learning was proposed. In this research, to evaluate the validity of the model, we executed the motor control and learning simulation and compare results with the characteristics observed in human's movements. As the result, hand path and speed profile show the human characteristic. However, no coactivation was observed in the simulation. It suggest that this model need some mechanism which makes coactivation.

CiNii Books

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

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Publishing type：Research paper, summary (national, other academic conference)  

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Language：English   Publishing type：Research paper, summary (national, other academic conference)  

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

If you ride a bicycle on bumpy road, you may grip handle-bars to prepare for unpredictable impulse. Although we know that human controls joint impedance based on prediction-uncertainty, it wasn't revealed how our brain acquires that control strategy. To reveal the learning mechanism, we executed both an experiment and a computer simulation. In the experiment, subjects tried ball-catching in several uncertainty environments and showed positive correlation between uncertainty and their wrist joint impedance. In the simulation, our proposed model tried same task and acquired a control strategy like human. These results suggest human control joint impedance optimally based on trade-off between movement accuracy and energy consumption in uncertainty environment.

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Publishing type：Research paper, summary (national, other academic conference)  

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

Recently, Brain-Computer Interfaces (BCI) are used for handicapped individuals such as patients that have difficulty in communicating. In this study, we explored the possibilities of non-invasive BCIs for speech identification. Based on electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), EEG cortical current signals were estimated using Variational Bayesian Multimodal Encephalo Graphy (VBMEG) method. The results of experiments with Japanese vowels /a/ and /i/ showed that classification accuracies might be increased when using anatomical-based functional region of interests (ROIs).

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Language：English   Publishing type：Research paper, summary (international conference)  

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J-GLOBAL

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Language：English   Publisher：一般社団法人電子情報通信学会  

Electrocorticography (ECoG) has drawn attention as an effective recording approach for less invasive brain-machine interfaces (BMI). Previous studies succeeded in classifying the movement direction and predicting hand trajectories from ECoGs. Despite such successful studies, there still remain considerable works for the purpose of realizing an ECoG-based BMI robot. We developed a method to predict multiple muscle activities from ECoG measurements. We also verified that ECoG signals could be effective for predicting muscle activities in time varying series for preforming sequential movements. Each ECoG signal was filtered by different bandpass filters for sensorimotor rhythms, normalized by the standard z-score, and smoothed by a Gaussian filter. We used sparse linear regression to find the best fit between frequency bands of ECoG and electromyogram (EMG). We also predicted angle of 4 DOF robot arm from the decoded EMG using 3-layer neural network. Consequently, this study shows that it could derive online prediction of angle of robot arm from ECoG signals.

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Language：English   Publisher：The Institute of Electronics, Information and Communication Engineers  

Electrocorticography (ECoG) has drawn attention as an effective recording approach for less invasive brain-machine interfaces (BMI). Previous studies succeeded in classifying the movement direction and predicting hand trajectories from ECoGs. Despite such successful studies, there still remain considerable works for the purpose of realizing an ECoG-based BMI robot. We developed a method to predict multiple muscle activities from ECoG measurements. We also verified that ECoG signals could be effective for predicting muscle activities in time varying series for preforming sequential movements. Each ECoG signal was filtered by different bandpass filters for sensorimotor rhythms, normalized by the standard z-score, and smoothed by a Gaussian filter. We used sparse linear regression to find the best fit between frequency bands of ECoG and electromyogram (EMG). We also predicted angle of 4 DOF robot arm from the decoded EMG using 3-layer neural network. Consequently, this study shows that it could derive online prediction of angle of robot arm from ECoG signals.

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Language：Japanese  

J-GLOBAL

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Language：Japanese  

CiNii Books

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

In previous reported P300-or SSVEP-based BCIs, low frequency flicker stimulus (10-30Hz) had been employed frequently. However, the stimulus is not always comfortable for user, because it may cause eye strain and photosensitive epilepsy. Then, we tried to construct a SSVEP-based BCI using high frequency (40Hz-) flicker stimulus to reduce the eye strain and estimate visual attentions accurately.

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Other Link： http://search.jamas.or.jp/link/ui/2013094473

Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

When holding some object, your brain has two works;feeling object's weight, or perception, and sending motor commands to your body, or motor control.Perception is influenced by not only sensory inputs but also expectation of object's weight. On the other hand, it's not clear how does expectation influence on motor control. In this study, I researched how expectation influences on motor control, by measuring stiffness and equilibrium position, which are calculated from EMG. As the result, they shows "anti-Bayesian" effects in holding weight task perception, which suggested that there are some relation between perception and motor command.

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Other Link： http://search.jamas.or.jp/link/ui/2013094477

Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

Although the size-weight illusion, a famous phenomenon related to weight perception, has been known for over 100 years, we still lack a good explanation of why it occurs. To clarify the neural mechanism underlying the illusion, it is necessary to find out what kinds of informations are involved in and essential for weight perception. To reveal this mechanism, we asked the subjects to hold an object which has same size and various weights. We measured EMG activities of the forearm and evaluate weight perception with different stiffliess level during the task. From these results, the sensitiv...

J-GLOBAL

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Language：English  

J-GLOBAL

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

Electroencephalography-based brain-machine interfaces (EEG-based BMIs) have recently been able to discriminate movement direction of body parts, such as hands and legs. However, there are no studies, except our latest report, which successfully reconstructed muscle activity time series from EEG. Here we showed that agonist and antagonist muscle activities during isometric contraction wrist movement could be reconstructed from EEG cortical currents estimated using VBMEG (Variational Bayesian Multimodal EncephaloGraphy) toolbox. Moreover, for a real-time application purpose, reconstruction ac...

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Language：Japanese  

指文字の認識は、ビデオ画像を入力として使うより、直接的に手指の姿勢が推定できる方法を導入する方が認識率の向上に寄与できる。本研究では前腕に表面電極を貼付して計測された表面筋電信号から、全波整流及び加算平均を行った積分筋電図信号を算出し、手指の姿勢推定をニューラルネットワークによって行う。推定した各指の姿勢から、木構造の分類アルゴリズムを用いて、指文字の認識を行う。これまでに「あ、い、う、え、お」の認識を行ったが、対象を文字中の動作を含まない日本語指文字まで広げ認識を行って検証した。

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Language：English   Publishing type：Research paper, summary (international conference)  

DOI： 10.1016/j.neures.2011.07.880

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Language：English   Publishing type：Research paper, summary (international conference)  

DOI： 10.1016/j.neures.2011.07.1088

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Language：English   Publishing type：Research paper, summary (international conference)  

DOI： 10.1016/j.neures.2011.07.878

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Language：English   Publishing type：Research paper, summary (international conference)  

DOI： 10.1016/j.neures.2011.07.1144

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Language：Japanese   Publisher：計測自動制御学会  

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Language：Japanese   Publisher：The Acoustical Society of Japan (ASJ)  

DOI： 10.20697/jasj.66.11_577

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Language：Japanese  

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Language：English   Publishing type：Research paper, summary (international conference)  

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Language：Japanese   Publisher：Information Processing Society of Japan (IPSJ)  

Pseudo-haptics is a phenomenon where visuo-haptic sensory conflicts and the sensory dominance of vision over touch cause a haptic illusion when perceiving spatial properties, such as distance, position, size, texture, and mass. That is, one perceives a haptic property different from the one present in the real world when appropriate visual feedback is provided. The goal of our research is to identify a set of principles for visual interaction design to communicate "weight" by using the pseudo-haptics feedback. This paper lists a set of factors that need to be studied, and describes the IWE ...

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

This research proposes a new model how we learn an acceleration in ball catching. This proposed model has two or more TTC(time to contact) calculators. A TTC calculator relates to an acceleration, and can calculate TTC of falling object in this acceleration. Therefore this model can do learning and the forecast of two or more accelerations. I experiment by using the virtual environment for the verification. And, the result of supporting the proposed model was obtained.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

The way of present power assist is to estimate user's movement pattern and make him follow one constant movement pattern. The problem is to depend on adaptation of user's ability so that he must one-sidedly adapt the assist system. This is great different from the support of the assistance and creation of the motor function through mutual entrainment like rehabilitation. So, I aim to achieve to build a relationship of mutual adaptation between user and powered suits generating appropriate power for his movement assist, estimated from his myoelectric signal. My study is to propose the power ...

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

It is important for advance of myoelectric prosthetic hand to establish method of finger posture estimation with surface EMG. This report proposes a new estimation method which uses ICA(Independent Component Analysis). ICA decomposes observed EMG to activation of virtual muscles which has function of multi real muscles. So, if measurement places of EMG are rough and few, decomposed signals show enough information for estimation, and this method will achieve a balance between accuracy and easiness of measurement.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

It is suggested that a similar movement to the teaching motion be achieved by teaching a muscular usage when the person teaches operating from recent research results. For instance, the method of teaching motion that presents a muscular active state as intuitive sense information is proposed by using functional electrical stimulation (FES). It is thought that motion can be taught more effectively than the methods of teaching only FES by adding the feedback of visual information to the method of teaching motion. Then, some experiments were done by using the system that positively presented s...

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Language：English   Publishing type：Research paper, summary (international conference)  

DOI： 10.1016/j.neures.2010.07.2519

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Language：English   Publishing type：Research paper, summary (international conference)  

DOI： 10.1016/j.neures.2010.07.2229

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Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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