19K11428:Anthropomorphic robot hand

—Prosthetic hands are artificial extensions that help people who have lost their hands or arms to regain normal activity. One of the main requirements is that it should be as close as possible to the natural hand. Human hand flexibility is largely due to our highly evolved hand structure. To achieve those structures, we have adopted a human-like design concept. We designed the prosthetic hand based on the salient features of the human anatomy, making it have the same structural features as human hands: artificial joints, ligaments and Extensor hood, that should be very similar to the real hands. We tested to control prosthetic hand based on detected coordinates by Leap Motion. Finally, we could succeed in artificial hands to achieve same flexibility as characteristics of the human hand.

Sensors & Materials 2019

Quadcopter Robot Control Based on Hybrid Brain–Computer Interface System [PDF]

Chao Chen, Peng Zhou, Abdelkader Nasreddine Belkacem, Lin Lu, Rui Xu, Xiaotian Wang, Wenjun Tan, Zhifeng Qiao, Penghai Li, Qiang Gao, and Duk Shin

(Received July 12, 2019; Accepted November 5, 2019)

Keywords: hybrid brain computer interface (hBCI), common spatial pattern (CSP), hierarchical support vector machine (hSVM)

A hybrid brain–computer interface (hBCI) has recently been proposed to address the limitations of existing single-modal brain computer interfaces (BCIs) in terms of accuracy and information transfer rate (ITR) by combining more than one modality. The hBCI system also showed promising prospects for patients because the design of a human-centered smart robot control system may allow the performance of multiple tasks with high efficiency. In this paper, we present a hybrid multicontrol system that simultaneously uses electroencephalography (EEG) and electrooculography (EOG) signals. After the preprocessing phase, we used a common spatial pattern (CSP) algorithm to extract EEG and EOG features from motor imagery and eye movements. Moreover, a support vector machine (SVM) was used to solve a multiclass problem and complete flight operations through the asynchronous hBCI control of a four-axis quadcopter (e.g., takeoff, forward, backward, rightward, leftward, and landing). Online decoding of experimental results showed that 97.14, 95.23, 98.09, and 96.66% average accuracies, and 45.80, 43.99, 46.78, and 45.34 bits/min average ITRs were achieved in the control of a quadcopter. These online experimental results showed that the proposed hybrid system might be better in terms of completing multidirection control tasks to increase the multitasking and dimensionality of a BCI.

Corresponding author: Qiang Gao and Duk Shin


The International Symposium on Community-centric Systems (CcS 2020) is organized by the Research Center for Community-centric Systems, Tokyo Metropolitan University. The conference will be held in Tokyo, Japan, from September 23rd to 26th, 2020. The conference offers a unique and interesting platform for scientists, engineers and practitioners throughout the world to present and share their recent research and innovative ideas on community-centric systems (CcS) that can improve quality of life (QOL) and quality of community (QOC).

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.


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

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


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