16K01572:Robot hand using EEG signals

Development of a support robot hand system using SSVEP

Zixun He, Yuusuke Watanabe, Rezenko Roman Yurievich, Yuta Ogai, Yousun Kang, and Duk Shin

Recently, the Brain-Computer Interface (BCI) system could support various aspects of everyday life of elderly and disabled people. In this research, we developed a noninvasive BCI system that controls the robot hand using induced brain waves Steady-State Visual Evoked Potential (SSVEP) in order to improve the quality of life of patients with hands or arms deficient or impaired. This BCI system consists of visual stimulator, 6 degree of freedom (DOF) robot hand, an EEG recorder and a laptop for processing data. The subject induces the corresponding SSVEP signal by seeing one target in the three visual stimuli (5Hz, 6Hz, 7Hz) representing the motion: grip, pinch and arm rotation of the robot hand. The detected SSVEP signal is classified by canonical correlation analysis (CCA). The robot hand is operated by converting the SSVEP into the control signal according to the classification result. The results show that the proposed BCI system has a high performance, achieving the average accuracy of 97% in a time window length of 4 s and the use of three harmonics.

http://isyou.info/inpra/papers/inpra-v6n4-01.pdf

This work was supported by KAKENHI grants (16K01572), and ‘FY2016 MEXT Private University Research Branding Project’.

About me

“20 years in biomedical signal processing and control focusing on HCI/BMI using EEG, EMG, ECG, EOG and ECoG”

    Dr. Shin is now a professor at Dept. of Electronics and Mechatronics of Tokyo Polytechnic University.

    His research interests include wearable robot, human centered system, human computer interface,  brain machine (ie.computer) interface, and bio-signal engineering.

    He is a member of Society for Neuroscience (SfN), the Japan Neuroscience Society (JNS). He is also an editorial board member of Scientific Reports that is a sister journal of the Nature.

私は2001年来日、東京工業大学で筋肉の数理モデルを提案しPh.D.を取りました。その後、同大学の研究員と助手、豊田中央研究所、精密工学研究所を経て2015年に本学へ着任しました。これまで、エアードラムや念力システム、居眠り事故の防止システムなど様々なヒューマン・コンピュータ・インタフェース(HCI)の開発に従事しました。
 最近ではサルや人間の脳神経活動から運動情報と力学情報を機械学習を用いて予測する手法を提案し、ロボットアームを制御するブレイン・マシン・インタフェース(BMI)研究に従事しています。私は人間らしい動くロボットを実現させることが夢です。ウェアラブルロボット研究室の学生と共に一歩一歩あゆみたいです。

RESEARCH INTERESTS

Wearable Robot

power suit, prosthetic armDOWNLOAD FULL CV

Brain Machine Interface (BMI/BCI)

simultaneous EMG, and ECoG to investigate the neural mechanisms of arm control

Human Computer Interface (HCI)

new interface with EMGs such as Air drum

Designed and implemented a muscular–skeletal model

Driver Monitoring System

Designed and implemented bio-signal processing algorithms

EDUCATION

 2001–2005   Ph.D. 

Tokyo Institute of Technology, Japan.

 “Arm Stiffness Estimation using Mathematical Model of Musculo-skeletal System”

 1996–1998   M.S. 

Chosun University, Korea.

 “Design of a Force-reflecting Haptic Interface using Ultrasonic Motors”

 1992–1996   B.S

Chosun University, Korea.

Work Experience

2020-present Professor of Tokyo Polytechnic University

2015- 2020  Associate Professor of Tokyo Polytechnic University

2011–2015   Research Associate Professor  of Tokyo Institute of Technology, Yokohama.

2010–2011   Research Assistant Professor 

Tokyo Institute of Technology, Yokohama.

2007–2010   Visiting researcher,

Toyota Central R & D Laboratory, Inc., Nagakute.

2006–2007   Assistant Professor

Tokyo Institute of Technology, Yokohama.

2005–2006   Researcher

Tokyo Institute of Technology, Yokohama.

Journal of Healthcare Engineering 2019

G-Causality Brain Connectivity Differences of Finger Movements between Motor Execution and Motor Imagery

Chao Chen, Jiaxin Zhang, Abdelkader Nasreddine Belkacem, Shanting Zhang, Rui Xu, Bin Hao, Qiang Gao, Duk Shin, Changming Wang, Dong Ming

Abstract

Motor imagery is one of the classical paradigms which have been used in brain-computer interface and motor function recovery. Finger movement-based motor execution is a complex biomechanical architecture and a crucial task for establishing most complicated and natural activities in daily life. Some patients may suffer from alternating hemiplegia after brain stroke and lose their ability of motor execution. Fortunately, the ability of motor imagery might be preserved independently and worked as a backdoor for motor function recovery. The efficacy of motor imagery for achieving significant recovery for the motor cortex after brain stroke is still an open question. In this study, we designed a new paradigm to investigate the neural mechanism of thirty finger movements in two scenarios: motor execution and motor imagery. Eleven healthy participants performed or imagined thirty hand gestures twice based on left and right finger movements. The electroencephalogram (EEG) signal for each subject during sixty trials left and right finger motor execution and imagery were recorded during our proposed experimental paradigm. The Granger causality (G-causality) analysis method was employed to analyze the brain connectivity and its strength between contralateral premotor, motor, and sensorimotor areas. Highest numbers for G-causality trials of 37 ± 7.3, 35.5 ± 8.8, 36.3 ± 10.3, and 39.2 ± 9.0 and lowest Granger causality coefficients of 9.1 ± 3.2, 10.9 ± 3.7, 13.2 ± 0.6, and 13.4 ± 0.6 were achieved from the premotor to motor area during execution/imagination tasks of right and left finger movements, respectively. These results provided a new insight into motor execution and motor imagery based on hand gestures, which might be useful to build a new biomarker of finger motor recovery for partially or even completely plegic patients. Furthermore, a significant difference of the G-causality trial number was observed during left finger execution/imagery and right finger imagery, but it was not observed during the right finger execution phase. Significant difference of the G-causality coefficient was observed during left finger execution and imagery, but it was not observed during right finger execution and imagery phases. These results suggested that different MI-based brain motor function recovery strategies should be taken for right-hand and left-hand patients after brain stroke.

Volume 2019 |Article ID 5068283