Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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

DOI： 10.1109/bci65088.2025.10931318

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

DOI： 10.1016/j.chaos.2024.115886

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

Transcranial direct current stimulation (tDCS) is a potential method for improving verbal function by stimulating Broca's area. Previous studies have shown the effectiveness of using functional magnetic resonance imaging (fMRI) to optimize the stimulation site, but it is unclear whether similar optimization can be achieved using scalp electroencephalography (EEG). Here, we investigated whether tDCS targeting a brain area identified by EEG can improve verbalization performance during a picture-naming task. In Experiment 1, EEG and fMRI data were acquired during a naming task with 21 participants. Comparison of EEG and fMRI data showed overlap in the highest areas of activation for 80% of the participants. In Experiment 2, tDCS was administered to 15 participants using a crossover design, with stimulation targeting the EEG-guided area, Broca's area, and sham conditions. Our findings indicated that tDCS targeting the EEG-guided area significantly improved lexical retrieval speed compared with stimulation over Broca's area and sham conditions. These results support the validity of EEG-based area identification and its use in optimizing the effects of tDCS on improving language function.

DOI： 10.1016/j.neuroimage.2024.120997

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

DOI： 10.1016/j.neunet.2024.106899

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/VRW66409.2025.00185

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Other Link： https://dblp.uni-trier.de/db/conf/vr/vr2025w.html#ChoKKTKSKMMIY25

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-3-031-92710-2_18

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Other Link： https://dblp.uni-trier.de/db/conf/hci/hci2025-45.html#InagakiSY25

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

DOI： 10.1109/apsipaasc63619.2025.10848702

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

DOI： 10.1109/bmeicon64021.2024.10896292

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

Sensory integration is an essential human function whose decline impacts quality of life, particularly in older adults. Herein, we propose an arm-reaching task based on a virtual reality head-mounted display system to assess sensory integration in daily life, and we examined whether reaching task performance was associated with resting-state functional connectivity (rsFC) between the brain regions involved in sensory integration. We hypothesized that declining sensory integration would affect performance during a reaching task with multiple cognitive loads. Using a task in which a young/middle-aged group showed only small individual differences, older adults showed large individual differences in the gap angle between the reaching hand and the target position, which was used to assess sensory integration function. Additionally, rsfMRI data were used to identify correlations between rsFC and performance in older adults, showing that performance was correlated with connectivity between the primary motor area and the left inferior temporal gyrus and temporo-occipital region. Connectivity between areas is related to visuomotor integration; thus, the results suggest the involvement of visuomotor integration in the decline of sensory integration function and the validity of the gap angle during this VR reaching task as an index of functional decline.

DOI： 10.3390/brainsci14080840

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Public Library of Science (PLoS)  

Thermal comfort of humans depends on the surrounding environment and affects their productivity. Several environmental factors, such as air temperature, relative humidity, wind or airflow, and radiation, have considerable influence on the thermal comfort or pleasantness; hence, these are generally controlled by electrical devices. Lately, the development of objective measurement methods for thermal comfort or pleasantness using physiological signals is receiving attention to realize a personalized comfortable environment through the automatic control of electrical devices. In this study, we focused on electroencephalography (EEG) and investigated whether EEG signals contain information related to the pleasantness of ambient airflow reproducing natural wind fluctuations using machine learning methods. In a hot and humid artificial climate chamber, we measured EEG signals while the participants were exposed to airflow at four different velocities. Based on the reported pleasantness levels, we performed within-participant classification from the source activity of the EEG and obtained a classification accuracy higher than the chance level using both linear and nonlinear support vector machine classifiers as well as an artificial neural network. The results of this study showed that EEG is useful in identifying people’s transient pleasantness when exposed to wind.

DOI： 10.1371/journal.pone.0299036

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DOI： 10.48550/arXiv.2404.15309

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

DOI： 10.1145/3652920.3653047

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Other Link： https://dblp.uni-trier.de/rec/conf/aughuman2/2024

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

Autism spectrum disorder (ASD) is a series of neurodevelopmental disorders that may affect a patient's social, behavioral, and communication abilities. As a typical mental illness, ASD is not a single disorder. ASD is often divided into subtypes, such as autism, Asperger's, and pervasive developmental disorder-not otherwise specified (PDD-NOS). Studying the differences among brain networks of the subtypes has great significance for the diagnosis and treatment of ASD. To date, many studies have analyzed the brain activity of ASD as a single mental disorder, whereas few have focused on its subtypes. To address this problem, we explored whether indices derived from functional and structural magnetic resonance imaging (MRI) data exhibited significant dissimilarities between subtypes. Utilizing a brain pattern feature extraction method from fMRI based on tensor decomposition, amplitude of low-frequency fluctuation and its fractional values of fMRI, and gray matter volume derived from MRI, impairments of function in the subcortical network and default mode network of autism were found to lead to major differences from the other two subtypes. Our results provide a systematic comparison of the three common ASD subtypes, which may provide evidence for the discrimination between ASD subtypes.

DOI： 10.3389/fnins.2024.1440222

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

To explore whether temporal electroencephalography (EEG) traits can dissociate the physical properties of touching objects and the congruence effects of cross-modal stimuli, we applied a machine learning approach to two major temporal domain EEG traits, event-related potential (ERP) and somatosensory evoked potential (SEP), for each anatomical brain region. During a task in which participants had to identify one of two material surfaces as a tactile stimulus, a photo image that matched ('congruent') or mismatched ('incongruent') the material they were touching was given as a visual stimulus. Electrical stimulation was applied to the median nerve of the right wrist to evoke SEP while the participants touched the material. The classification accuracies using ERP extracted in reference to the tactile/visual stimulus onsets were significantly higher than chance levels in several regions in both congruent and incongruent conditions, whereas SEP extracted in reference to the electrical stimulus onsets resulted in no significant classification accuracies. Further analysis based on current source signals estimated using EEG revealed brain regions showing significant accuracy across conditions, suggesting that tactile-based object recognition information is encoded in the temporal domain EEG trait and broader brain regions, including the premotor, parietal, and somatosensory areas.

DOI： 10.3390/s24010045

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

DOI： 10.1109/bmeicon60347.2023.10321978

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

DOI： 10.1109/bmeicon60347.2023.10322031

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Language：English   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

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DOI： 10.1109/TBME.2023.3246599

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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)  

BACKGROUND: Parkinson's disease (PD) is a long-term neurodegenerative disease of the central nervous system. The current diagnosis is dependent on clinical observation and the abilities and experience of a trained specialist. One of the symptoms that affects most patients is voice impairment. OBJECTIVE: Voice samples are non-invasive data that can be collected remotely for diagnosis and disease progression monitoring. In this study, we analyzed voice recording data from a smartphone as a possible medical self-diagnosis tool by using only one-second voice recording. The data from one of the largest mobile PD studies, the mPower study, was used. METHODS: A total of 29,798 ten-second voice recordings on smartphone from 4,051 participants were used for the analysis. The voice recordings were from sustained phonation by participants saying /aa/ for ten seconds into an iPhone microphone. A dataset comprising 385,143 short one-second audio samples was generated from the original ten-second voice recordings. The samples were converted to a spectrogram using a short-time Fourier transform. CNN models were then applied to classify the samples. RESULTS: Classification accuracies of the proposed method with LeNet-5, ResNet-50, and VGGNet-16 are 97.7 ± 0.1%, 98.6 ± 0.2%, and 99.3 ± 0.1%, respectively. CONCLUSIONS: We achieve a respectable classification performance using a generalized approach on a dataset with a large number of samples. The result emphasizes that an analysis based on one-second clip recorded on a smartphone could be a promising non-invasive and remotely available PD biomarker.

DOI： 10.3233/THC-220386

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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

DOI： 10.1016/j.chaos.2023.113100

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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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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

This study explores brain-network differences between the intrinsic and extrinsic motor coordinate frames. A connectivity model showing the coordinate frames difference was obtained using brain fMRI data of right wrist isometric flexions and extensions movements, performed in two forearm postures. The connectivity model was calculated by machine-learning-based neural representation and effective functional connectivity using psychophysiological interaction and dynamic causal modeling analyses. The model indicated the network difference wherein the inferior parietal lobule receives extrinsic information from the rostral lingual gyrus through the superior parietal lobule and transmits intrinsic information to the Handknob, whereas extrinsic information is transmitted to the Handknob directly from the rostral lingual gyrus. A behavioral experiment provided further evidence on the difference between motor coordinate frames showing onset timing delay of muscle activity of intrinsic coordinate-directed wrist movement compared to extrinsic one. These results suggest that, if the movement is externally directed, intrinsic coordinate system information is bypassed to reach the primary motor area.

DOI： 10.3389/fninf.2023.1199862

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

DOI： 10.3390/s23010277

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

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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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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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

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

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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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press ({OUP})  

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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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier {BV}  

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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Publishing type：Research paper (other academic)  

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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)   Publisher：{MDPI} {AG}  

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

DOI： 10.1080/01691864.2021.1900913

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

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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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Wiley  

DOI： 10.1002/aisy.202000164

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

Patients in completely locked-in state (CLIS) due to amyotrophic lateral sclerosis (ALS) lose the control of each and every muscle of their body rendering them motionless and without any means of communication. Though some studies have attempted to develop brain-computer interface (BCI)-based communication methods with CLIS patients, little information is available of the neuroelectric brain activity of CLIS patients. However, because of the difficulties with and often loss of communication, the neuroelectric signature may provide some indications of the state of consciousness in these patients. We recorded electroencephalography (EEG) signals from 10 CLIS patients during resting state and compared their power spectral densities with those of healthy participants in fronto-central, central, and centro-parietal channels. The results showed significant power reduction in the high alpha, beta, and gamma bands in CLIS patients, indicating the dominance of slower EEG frequencies in their oscillatory activity. This is the first study showing group-level EEG change of CLIS patients, though the reason for the observed EEG change cannot be concluded without any reliable communication methods with this population.

DOI： 10.1016/j.neures.2020.01.013

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

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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Language：English   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

Language：English   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

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：{MDPI} {AG}  

Age-related decline in sensorimotor integration involves both peripheral and central components related to proprioception and kinesthesia. To explore the role of cortical motor networks, we investigated the association between resting-state functional connectivity and a gap-detection angle measured during an arm-reaching task. Four region pairs, namely the left primary sensory area with the left primary motor area (S1left-M1left), the left supplementary motor area with M1left (SMAleft-M1left), the left pre-supplementary motor area with SMAleft (preSMAleft-SMAleft), and the right pre-supplementary motor area with the right premotor area (preSMAright-PMdright), showed significant age-by-gap detection ability interactions in connectivity in the form of opposite-sign correlations with gap detection ability between younger and older participants. Morphometry and tractography analyses did not reveal corresponding structural effects. These results suggest that the impact of aging on sensorimotor integration at the cortical level may be tracked by resting-state brain activity and is primarily functional, rather than structural. From the observation of opposite-sign correlations, we hypothesize that in aging, a "low-level" motor system may hyper-engage unsuccessfully, its dysfunction possibly being compensated by a "high-level" motor system, wherein stronger connectivity predicts higher gap-detection performance. This hypothesis should be tested in future neuroimaging and clinical studies.

DOI： 10.3390/brainsci10120966

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

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

DOI： 10.1109/biorob49111.2020.9224321

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

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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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：{MDPI} {AG}  

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

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

DOI： 10.1007/s11071-020-05581-y

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.18494/SAM.2020.2636

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Language：English   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 (scientific journal)  

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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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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Muscle functional MRI (mfMRI) is an imaging technique that assess muscles' activity, exploiting a shift in the T2-relaxation time between resting and active state on muscles. It is accompanied by the use of electromyography (EMG) to have a better understanding of the muscle electrophysiology; however, a technique merging MRI and EMG information has not been defined yet. In this paper, we present an anatomical and quantitative evaluation of the method our group introduced in to quantify its validity in terms of muscle pattern estimation for four subjects during four isometric tasks. Muscle activation pattern are estimated using a resistive network to model the morphology in the MRI. An inverse problem is solved from sEMG data to assess muscle activation. The results have been validated with a comparison with physiological information and with the fitting on the electrodes space. On average, over 90% of the input sEMG information was able to be explained with the estimated muscle patterns. There is a match with anatomical information, even if a strong subjectivity is observed among subjects. With this paper we want to proof the method's validity showing its potential in diagnostic and rehabilitation fields.

DOI： 10.3390/s20030724

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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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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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Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease affecting the corticospinal tract and leading to motor neuron death. According to a recent study, magnetic resonance imaging-visible changes suggestive of neurodegeneration seem absent in the motor cortex of G93A-SOD1 ALS mice. However, it has not yet been ascertained whether the cortical neural activity is intact, or alterations are present, perhaps even from an early stage. Here, cortical neurons from this model were isolated at post-natal day 1 and cultured on multielectrode arrays. Their activity was studied with a comprehensive pool of neurophysiological analyses probing excitability, criticality and network architecture, alongside immunocytochemistry and molecular investigations. Significant hyperexcitability was visible through increased network firing rate and bursting, whereas topological changes in the synchronization patterns were apparently absent. The number of dendritic spines was increased, accompanied by elevated transcriptional levels of the DLG4 gene, NMDA receptor 1 and the early pro-apoptotic APAF1 gene. The extracellular Na+, Ca2+, K+ and Cl- concentrations were elevated, pointing to perturbations in the culture micro-environment. Our findings highlight remarkable early changes in ALS cortical neuron activity and physiology. These changes suggest that the causative factors of hyperexcitability and associated toxicity could become established much earlier than the appearance of disease symptoms, with implications for the discovery of new hypothetical therapeutic targets.

DOI： 10.1016/j.neuroscience.2019.07.041

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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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Language：English   Publishing type：Research paper (scientific journal)   Publisher：{AIP} Publishing  

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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DOI： 10.1109/ACCESS.2019.2912903

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

DOI： 10.1109/ACCESS.2019.2957014

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

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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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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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DOI： 10.1038/s41598-017-09770-5

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DOI： 10.1038/srep45486

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DOI： 10.1080/01691864.2016.1215935

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

DOI： 10.1109/mhs.2016.7824149

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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： 10.1016/j.bspc.2014.10.005

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DOI： 10.1155/2015/653639

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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： 10.5405/jmbe.1596

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DOI： 10.1016/j.neures.2014.03.010

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

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

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

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

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：International Journal of Engineering and Technology (IJET)  

DOI： 10.7763/IJET.2014.V6.680

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：International Information Institute Ltd.  

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DOI： 10.3389/fpsyg.2013.00040

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DOI： 10.1371/journal.pone.0083534

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DOI： 10.1371/journal.pone.0072085

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Language：Japanese   Publisher：日本リハビリテーション医学会 ; 2007-  

DOI： 10.2490/jjrmc.49.704

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Other Link： http://search.jamas.or.jp/link/ui/2013041718

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DOI： 10.1016/j.neuroimage.2011.08.029

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DOI： 10.1016/j.neures.2011.07.874

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DOI： 10.1109/ICVR.2011.5971870

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Polyglutamine tract-binding protein-1 (PQBP1) is involved in the transcription-splicing coupling, and its mutations cause a group of human mental retardation syndromes. We generated a fly model in which the Drosophila homolog of PQBP1 (dPQBP1) is repressed by insertion of piggyBac. In classical odor conditioning, learning acquisition was significantly impaired in homozygous piggyBac-inserted flies, whereas the following memory retention was completely normal. Mushroom bodies (MBs) and antennal lobes were morphologically normal in dPQBP1-mutant flies. Projection neurons (PNs) were not reduced in number and their fiber connections were not changed, whereas gene expressions including NMDA receptor subunit 1 (NR1) were decreased in PNs. Targeted double-stranded RNA-mediated silencing of dPQBP1 in PNs, but not in MBs, similarly disrupted learning acquisition. NR1 overexpression in PNs rescued the learning disturbance of dPQBP1 mutants. HDAC (histone deacetylase) inhibitors, SAHA (suberoylanilide hydroxamic acid) and PBA (phenylbutyrate), that upregulated NR1 partially rescued the learning disturbance. Collectively, these findings identify dPQBP1 as a novel gene regulating learning acquisition at PNs.

DOI： 10.1523/JNEUROSCI.1319-10.2010

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Non-cell-autonomous effect of mutant proteins expressed in glia has been implicated in several neurodegenerative disorders, whereas molecules mediating the toxicity are currently not known. We identified a novel molecule named multiple alpha-helix protein located at ER (Maxer) downregulated by mutant ataxin-1 (Atx1) in Bergmann glia. Maxer is an endoplasmic reticulum (ER) membrane protein interacting with CDK5RAP3. Maxer anchors CDK5RAP3 to the ER and inhibits its function of Cyclin D1 transcription repression in the nucleus. The loss of Maxer eventually induces cell accumulation at G1 phase. It was also shown that mutant Atx1 represses Maxer and inhibits proliferation of Bergmann glia in vitro. Consistently, Bergmann glia are reduced in the cerebellum of mutant Atx1 knockin mice before onset. Glutamate-aspartate transporter reduction in Bergmann glia by mutant Atx1 and vulnerability of Purkinje cell to glutamate are both strengthened by Maxer knockdown in Bergmann glia, whereas Maxer overexpression rescues them. Collectively, these results suggest that the reduction of Maxer mediates functional deficiency of Bergmann glia, and might contribute to the non-cell-autonomous pathology of SCA1.

DOI： 10.1038/emboj.2010.116

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DOI： 10.1016/j.neures.2010.07.1454

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PQBP1 (polyglutamine tract-binding protein 1) is a causative gene for a relatively frequent X-linked syndromic and non-syndromic mental retardation (MR). To analyze behavioral abnormalities of these patients from molecular basis, we developed a knock-down (KD) mouse model. The KD mice possess a transgene expressing 498 bp double-strand RNA that is endogenously cleaved to siRNA suppressing PQBP1 efficiently. After confirming that PQBP1 is selectively suppressed to nearly 50% of the control mice, we performed behavioral analyses of PQBP1-KD mice. The KD mice possessed normal ability in ordinary memory tests including water-maze test, whereas they showed abnormal anxiety-related behavior in light/dark exploration test and open-field test and showed obvious declines of anxiety-related cognition in the repetitive elevated plus maze or novel object recognition test. Correspondingly, we found c-fos upregulation and histone H3 acetylation after behavior tests were declined in neurons of amygdala, prefrontal cortex and hippocampus. Furthermore, we found that 4-phenylbutyric acid, an HDAC inhibitor, efficiently improved expression of these genes and rescued the abnormal phenotypes in adult PQBP1-KD mice. These results suggested that PQBP1 dysfunction in regulating gene expression might underlie the abnormal behavior and cognition of PQBP1-KD mice and that the recovery of expression of such PQBP1 target genes might improve the symptoms in adult patients.

DOI： 10.1093/hmg/ddp378

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DOI： 10.1038/ncb1553

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DOI： 10.1016/j.febslet.2006.03.056

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DOI： 10.1083/jcb.200509132

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DOI： 10.1111/j.1460-9568.2005.04339.x

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

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Language：Japanese   Publisher：日本建築学会  

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J-GLOBAL

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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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DOI： 10.1016/j.bspc.2015.01.006

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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：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：The Institute of Electronics, Information and Communication Engineers  

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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Other Link： http://search.jamas.or.jp/link/ui/2015261143

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

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：The Institute of Electronics, Information and Communication Engineers  

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：The Institute of Electronics, Information and Communication Engineers  

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：Information Processing Society of Japan (IPSJ)  

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.

CiNii Books

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

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  

CiNii Books

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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：Japanese  

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Other Link： http://search.jamas.or.jp/link/ui/2013213823

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

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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 accuracies were additionally investigated on condition that not fMRI activity information but anatomical information was used for cortical current estimation.

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

DOI： 10.1016/j.neures.2010.07.2396

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Language：Japanese   Publisher：Japanese Society for Medical and Biological Engineering  

It is necessary for brain-computer interfaces(BCIs) to be non-offensive devices for daily use to improve the quality of life of users, especially for the motor disabled. Some BCIs which are based on steady-state visual evoked potentials(SSVEPs), however, are unpleasant because users have to gaze at high-speed blinking light as visual stimuli. Furthermore, these kinds of BCIs may not be used as universal devices because SSVEPs are not detectable by some users. Considering these facts, we propose a novel BCI using a non-direct gazing method based on transient VEPs. This interface uses a low-speed blinking lattice pattern as visual stimuli, and users gaze at other visual targets displayed on the right and the left sides of the stimuli. The gaze direction was determined by the waveform difference of transient VEPs detected when users gazed at either target. This mechanism was established by the result of exploratory experiment that indicated transient VEPs were detected even when users did not gaze at stimuli directly and two different types of waveforms were shown depending on their gaze direction. Compared with SSVEP-based BCIs, the proposed BCI is less annoying because it uses a lowspeed blinking pattern as visual stimuli and users do not have to gaze at the stimuli directly. In addition, bipolar derivation could reduce unnecessary signals and the number of responses used for signal averaging to detect transient VEPs, which led to shorter detection time of the VEPs providing this interface with acceptable speed as a BCI in terms of determining gaze direction. Experiments with 7 volunteer subjects showed a 90% accuracy rate in gaze direction judgments. The result suggests that the proposed BCI can be used as a substitute for SSVEP-based BCIs.

DOI： 10.11239/jsmbe.46.202

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Other Link： http://search.jamas.or.jp/link/ui/2008270616

Language：English   Publisher：Japan Society of Physiological Anthropology  

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Other Link： http://search.jamas.or.jp/link/ui/2008276821

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

Web of Science

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

Web of Science

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

Web of Science

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