A novel calibration and task guidance framework for motor imagery BCI via a tendon vibration induced sensation with kinesthesia illusion

被引:36
作者
Yao, Lin [1 ]
Meng, Jianjun [1 ]
Sheng, Xinjun [1 ]
Zhang, Dingguo [1 ]
Zhu, Xiangyang [1 ]
机构
[1] Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, Shanghai 200030, Peoples R China
基金
中国国家自然科学基金;
关键词
brain-computer interface; illusory movement; imagined movement; BRAIN-COMPUTER INTERFACES; BETA-SYNCHRONIZATION; ERD/ERS PATTERNS; SET-UP; COMMUNICATION; MOVEMENT; EEG; STROKE; AREAS; STATE;
D O I
10.1088/1741-2560/12/1/016005
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Objective. Lack of efficient calibration and task guidance in motor imagery (MI) based brain-computer interface (BCI) would result in the failure of communication or control, especially in patients, such as a stroke with motor impairment and intact sensation, locked-in state amyotrophic lateral sclerosis, in which the sources of data for calibration may worsen the subsequent decoding. In addition, enhancing the proprioceptive experience in MI might improve the BCI performance. Approach. In this work, we propose a new calibrating and task guidance methodology to further improve the MI BCI, exploiting the afferent nerve system through tendon vibration stimulation to induce a sensation with kinesthesia illusion. A total of 30 subjects' experiments were carried out, and randomly divided into a control group (control-group) and calibration and task guidance group (CTG-group). Main results. Online experiments have shown that MI could be decoded by classifier calibrated solely using sensation data, with 8 of the 15 subjects in the CTG-Group above 80%, 3 above 95% and all above 65%. Offline chronological cross-validation analysis shows that it has reached a comparable performance with the traditional calibration method (F(1, 14) = 0.14, P = 0.7176). In addition, the discrimination accuracy of MI in the CTG-Group is significantly 12.17% higher on average than that in the control-group (unpaired-T test, P = 0.0086), and illusory sensation indicates no significant difference (unpaired-T test, p = 0.3412). The finding of the existed similarity of the discriminative brain patterns and grand averaged ERD/ERS between imagined movement (actively induced) and illusory movement (passively evoked) also validates the proposed calibration and task guidance framework. Significance. The cognitive complexity of the illusory sensation task is much lower and more objective than that of MI. In addition, subjects' kinesthetic experience mentally simulated during the MI task might be enhanced by accessing sensory experiences from the illusory stimulation. This sensory stimulation aided BCI design could help make MI BCI more applicable.
引用
收藏
页数:11
相关论文
共 50 条
[1]   On knowing how to do things: A theory of motor imagery [J].
Annett, J .
COGNITIVE BRAIN RESEARCH, 1996, 3 (02) :65-69
[2]  
[Anonymous], INTRO STAT PATTERN R
[3]   Brain-computer interfaces: communication and restoration of movement in paralysis [J].
Birbaumer, Niels ;
Cohen, Leonardo G. .
JOURNAL OF PHYSIOLOGY-LONDON, 2007, 579 (03) :621-636
[4]   Breaking the silence: Brain-computer interfaces (BCI) for communication and motor control [J].
Birbaumer, Niels .
PSYCHOPHYSIOLOGY, 2006, 43 (06) :517-532
[5]   The BCI competition III:: Validating alternative approaches to actual BCI problems [J].
Blankertz, Benjamin ;
Mueller, Klaus-Robert ;
Krusienski, Dean J. ;
Schalk, Gerwin ;
Wolpaw, Jonathan R. ;
Schloegl, Alois ;
Pfurtscheller, Gert ;
Millan, Jose D. R. ;
Schroeder, Michael ;
Birbaumer, Niels .
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2006, 14 (02) :153-159
[6]   Neurophysiological predictor of SMR-based BCI performance [J].
Blankertz, Benjamin ;
Sannelli, Claudia ;
Haider, Sebastian ;
Hammer, Eva M. ;
Kuebler, Andrea ;
Mueller, Klaus-Robert ;
Curio, Gabriel ;
Dickhaus, Thorsten .
NEUROIMAGE, 2010, 51 (04) :1303-1309
[7]   A brain-computer interface with vibrotactile biofeedback for haptic information [J].
Chatterjee, Aniruddha ;
Aggarwal, Vikram ;
Ramos, Ander ;
Acharya, Soumyadipta ;
Thakor, Nitish V. .
JOURNAL OF NEUROENGINEERING AND REHABILITATION, 2007, 4 (1)
[8]   Brain-computer interfaces in neurological rehabilitation [J].
Daly, Janis J. ;
Wolpaw, Jonathan R. .
LANCET NEUROLOGY, 2008, 7 (11) :1032-1043
[9]   Brain communication in the locked-in state [J].
De Massari, Daniele ;
Ruf, Carolin A. ;
Furdea, Adrian ;
Matuz, Tamara ;
van der Heiden, Linda ;
Halder, Sebastian ;
Silvoni, Stefano ;
Birbaumer, Niels .
BRAIN, 2013, 136 :1989-2000
[10]   EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing [J].
Delorme, Arnaud ;
Mullen, Tim ;
Kothe, Christian ;
Acar, Zeynep Akalin ;
Bigdely-Shamlo, Nima ;
Vankov, Andrey ;
Makeig, Scott .
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2011, 2011