Study on feature modulation of electroencephalogram induced by motor imagery under multi-modal stimulation_Journal of Biomedical Engineering

Authors：

 ZHAO Li , LI Xiaoqin , BIAN Yan , WANG Xuanfang , YANG Genghuang

Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin University of Technology and Education, Tianjin 300222, P.R.China;

Corresponding author：

ZHAO Li, Email: jinshihui@163.com

Keywords：

motor imagery; ERD; dynamic stimulation; activation

DOI：

10.7507/1001-5515.201708061

Video：

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Abstract Full text Figures/Tables Video References Cited by

Event-related desynchronization (ERD) is the basic feature of electroencephalogram (EEG), and the brain-computer interface based on motor imagery (MI-BCI) with the foundation of the analysis of ERD is of great significance in motor function recovery. The valid ERD characteristics extracted from EEG are the key to the performance of the BCI, so the study of which kind of stimulation mode can prompt subjects to generate more obvious characteristics of ERD is crucial. Four different stimulation modes are designed in this paper, and the effects of motion imagery tasks under static text stimulation, grip video stimulation, serial motion video stimulation of fingers as well as serial motion video stimulation of fingers with sound on the characteristics of ERD are analyzed. Combining the analysis of time-frequency spectrum, the power spectral density curve, ERD value and brain topographic map, it is shown that the ERD under serial motion video stimulation of fingers and serial motion video stimulation of fingers with sound modes is much stronger and has wider range of activation, and the BCI based on the analysis of ERD will have a better effect on practical application. As a result, the recognition and acceptance of the users of BCI system are improved in some extent.

Citation： ZHAO Li, LI Xiaoqin, BIAN Yan, WANG Xuanfang, YANG Genghuang. Study on feature modulation of electroencephalogram induced by motor imagery under multi-modal stimulation. Journal of Biomedical Engineering, 2018, 35(3): 343-349. doi: 10.7507/1001-5515.201708061 Copy

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17.	姜涛. 基于皮质脑电的脑运动区功能定位原理与算法研究. 广州: 华南理工大学, 2010.

1. 万柏坤, 刘延刚, 明东, 等. 基于脑电特征的多模式想象动作识别. 天津大学学报, 2010, 43(10): 895-900.
2. Lotte F, Congedo M, Lécuyer A, et al. A review of classification algorithms for EEG-based brain-computer interfaces. J Neural Eng, 2007, 4(2): R1-R13.
3. Mulder T. Motor imagery and action observation: cognitive tools for rehabilitation. J Neural Transm, 2007, 114(10): 1265-1278.
4. Caria A, Weber C, Brötz D, et al. Chronic stroke recovery after combined BCI training and physiotherapy: a case report. Psychophysiology, 2011, 48(4): 578-582.
5. Stępień M, Conradi J, Waterstraat G, et al. Event-related desynchronization of sensorimotor EEG rhythms in hemiparetic patients with acute stroke. Neurosci Lett, 2011, 488(1): 17-21.
6. Wolpaw J R, Birbaumer N, Mcfarland D J, et al. Brain–computer interfaces for communication and control. Clinical Neurophysiology, 2002, 113(6): 767-791.
7. Prasad G, Herman P, Coyle D, et al. Applying a brain-computer interface to support motor imagery practice in People with stroke for upper limb recovery: a feasibility study. J Neuroeng Rehabil, 2010, 7: 60.
8. Pfurtscheller G, Neuper C. Future prospects of ERD/ERS in the context of brain-computer interface (BCI) developments. Prog Brain Res, 2006, 159: 433-437.
9. Gomez-Rodriguez M, Peters J, Hill J, et al. Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery. J Neural Eng, 2011, 8(3): 036005.
10. Oostra K M, Vereecke A, Jones K, et al. Motor imagery ability in patients with traumatic brain injury. Arch Phys Med Rehabil, 2012, 93(5): 828-833.
11. Sun Yao, Wei Wei, Luo Zhizeng, et al. Improving motor imagery practice with synchronous action observation in stroke patients. Top Stroke Rehabil, 2016, 23(4): 245-253.
12. Liang Shuang, Choi K S, Qin Jing, et al. Improving the discrimination of hand motor imagery via virtual reality based visual guidance. Comput Methods Programs Biomed, 2016, 132: 63-74.
13. Wang B A, Viswanathan S, Abdollahi R O, al. Frequency-specific modulation of connectivity in the ipsilateral sensorimotor cortex by different forms of movement initiation. NeuroImage, 2017, 159:248-260.
14. Tangwiriyasakul C, Verhagen R, van Putten M J, et al. Importance of baseline in event-related desynchronization during a combination task of motor imagery and motor observation. J Neural Eng, 2013, 10(2): 026009.
15. Yokosawa K, Watanabe T, Kikuzawa D, et al. Amplitude modulation of alpha-band rhythm caused by mimic collision: MEG study//2013 35th annual international conference of the ieee engineering in medicine and biology society (EMBC), Osaka, 2013: 6187-6190.
16. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including Independent component analysis. J Neurosci Methods, 2004, 134(1): 9-21.
17. 姜涛. 基于皮质脑电的脑运动区功能定位原理与算法研究. 广州: 华南理工大学, 2010.

Journal of Biomedical Engineering

Study on feature modulation of electroencephalogram induced by motor imagery under multi-modal stimulation

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