SSSEP提升下肢MI-BCI系统性能及其多维脑电特征分析

doi:10.3969/j.issn.0258-8021.2021.04.06

Abstract
Figure/Table
References
Related Citation (6)

Download: PDF (9775 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Motor imagery(MI)-based brain-computer interface (MI-BCI) can decode motor intention of users, providing an additional interactive manner for patients who are unable to exercise autonomously and improving their lifestyle. To solve the key problem of low classification performance of lower limb MI-BCI, we designed a hybrid paradigm, i.e. MI joint somatosensory electrical stimulation (MI+ES) inducing steady state somatosensory evoked potential (SSSEP) for MI-BCI of lower limb. And the performance of MI+ES was compared with the traditional single paradigm (MI). Twenty right-handed healthy subjects were recruited to participate in the experiment, five of them participated in the verification test of optimal induced frequency and fifteen participated in the formal experiment. EEG data of the fifteen subjects were recorded under different conditions. Fast Fourier transform (FFT) and event-related spectral perturbation (ERSP) were used to extract EEG frequency domain response and time-frequency features. The multi-frequency power changes were calculated at alpha (8～14 Hz), low beta (15～24 Hz) and high beta (25～35 Hz) bands. In addition, the performance of lower limb MI-BCI was explored under different conditions of MI/MI+ES and feature extraction methods of CSP/FBCSP. Results showed that the somatosensory electrical stimulation strategy could induce obvious SSSEP features. The classification accuracy of MI+ES condition was significantly improved in reference to the single MI condition (P<0.001). The classification performance based on FBCSP method was significantly better than that of classical CSP method (P <0.01), the classify accuracy of CSP was 70.2% under MI+ES condition, while the accuracy of subject S15 was 84.2%. And the accuracy of FBCSP was 71.7%, the accuracy of subject S15 was 90%. In conclusion, this study preliminarily confirmed that the SSSEP could be evoked by the somatosensory electrical, and the hybrid paradigm could effectively improve the classification performance of lower limb MI-BCI, which could promote the practical development, even provide optimization methods of peripheroneural somatosensory stimulation regulation.

Key words： lower limb motor imagery brain-computer interface(BCI) steady state somatosensory evoked potential(SSSEP) event-related spectral perturbation classification and recognition

Received: 21 September 2020

PACS:

R318

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Zhang Lixin
	Chang Meirong
	Wang Zhongpeng
	Chen Long
	Ming Dong

Cite this article:

Zhang Lixin,Chang Meirong,Wang Zhongpeng, et al. Improve the Performance of Lower Limb MI-BCI System Based on SSSEP and its Multi-Dimensional EEG Feature Analysis[J]. Chinese Journal of Biomedical Engineering, 2021, 40(4): 429-437.

URL:

http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2021.04.06 OR http://cjbme.csbme.org/EN/Y2021/V40/I4/429

[1] Kraus D, Naros G, Bauer R, et al. Brain-robot interface driven plasticity: Distributed modulation of corticospinal excitability[J]. Neuroimage, 2016, 125:522-532.
[2] Wan Xin, Zhang Kezhong, Ramkumar S, et al. A review on electroencephalogram based brain computer interface for elderly disabled[J]. IEEE Access, 2019,7:36380-36387.
[3] 周鹏,刘静,王春方,等.磁诱导对脑卒中患者运动康复的影响研究[J]. 电子测量与仪器学报,2017, 31(8):1233-1238.
[4] Ng DWK, Goh SY. Indirect control of an autonomous wheelchair using SSVEP BCI[J]. Journal of Robotics and Mechatronics, 2020, 32(4):761-767.
[5] Gupta GS, Bhatnagar M, Ghosh S, et al. Design of control system for motor imagery based neuro-aid application[J]. Biomedical Engineering Applications Basis and Communications, 2019, 31(4):1950031.
[6] 王磊. 基于运动想象的脑电信号分类与脑机接口技术研究[D]. 天津:河北工业大学, 2008.
[7] Rouillard J, Duprès A, Cabestaing F, et al. Hybrid BCI coupling EEG and EMG for severe motor disabilities[J]. Procedia Manufacturing, 2015, 3:29-36.
[8] Liu Dong, Chen Weihai, Lee K, et al. Brain-actuated gait trainer with visual and proprioceptive feedback[J]. Journal of Neural Engineering, 2017, 14(5):056017.
[9] Yu Zhongliang, Li Lili, Song Jinchun, et al. The study of visual-auditory interactions on lower limb motor imagery[J]. Frontiers in Neuroscience, 2018, 12:509.
[10] Singh G. Somatosensory evoked potential monitoring[J]. Journal of Neuroanaesthesiology and Critical Care, 2016, 3(4):S97-S104.
[11] Kee YJ, Won DO, Lee SW. Classification of left and right foot movement intention based on steady-state somatosensory evoked potentials[C]// 2017 5th International Winter Conference on Brain-Computer Interface (BCI). Gangwon: Chinese Institute of Electronics, 2017:106-109.
[12] Sangtae A, Minkyu A, Hohyun C, et al. Achieving a hybrid brain-computer interface with tactile selective attention and motor imagery[J]. Journal of Neural Engineering, 2014, 11(6):066004.
[13] YI Weibo, Qiu Shuang, Wang Kun, et al. Enhancing performance of a motor imagery based brain-computer interface by incorporating electrical stimulation-induced SSSEP[J]. Journal of Neural Engineering, 2016, 14(2):026002.
[14] Muzyka IM, Estephan B. Somatosensory evoked potentials[J]. Handbook of Clinical Neurology, 2019, 160:523-540.
[15] 王仲朋,陈龙,何峰,等. 面向康复与辅助应用的脑-机接口趋势与展望[J]. 仪器仪表学报,2017,38(6):1307-1318.
[16] Comani S, Velluto L, Schinaia L, et al. Monitoring neuro-motor recovery from stroke with high-resolution EEG, robotics and virtual reality: A proof of concept[J]. IEEE Transactions on Neural Systems & Rehabilitation Engineering, 2015, 23(6):1106-1116.
[17] Zhao Li, Li Xiaoqin, Bian Yan. Extraction and analysis of steady state somatosensory evoked potentials in lower limbs[C]//2018 International Conference on Information, Electronic and Communication Engineering (IECE 2018). Beijing:DEStech Publications, 2018: 98-103.
[18] 李小芹. 基于体感诱发和运动想象的下肢侧向性识别研究[D]. 天津:天津职业技术师范大学, 2018.
[19] Salenius S, Schnitzler A, Salmelin R, et al. Modulation of human cortical rolandic rhythms during natural sensorimotor tasks [J]. Neuroimage, 1997, 5(3):221-228.
[20] Matamoros OM, Escobar JJM, Padilla RT, et al. Neurodynamics of patients during a dolphin-assisted therapy by means of a fractal intraneural analysis[J]. Brain Sciences, 2020, 10(6):403.
[21] Demuru M, Fraschini M. EEG fingerprinting: Subject specific signature based on the aperiodic component of power spectrum[J]. Computers in Biology and Medicine, 2020,2020:103748.
[22] Madiha T, Pavel MT, Milan S. Mu-Beta event-related (de)synchronization and EEG classification of left-right foot dorsiflexion kinaesthetic motor imagery for BCI[J]. PLoS ONE, 2020, 15(3):e0230184.
[23] 李鹏海,王丽余,刘瀛涛,等. 下肢运动想象和运动执行的EEG节律特征性研究[J]. 仪器仪表学报, 2018,39(3):207-214.
[24] Blankertz B, Muller KR, Krusienski DJ, et al. The BCI competition III: Validating alternative approaches to actual BCI problems[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006, 14(2):153-159.
[25] Cho P, Chang W, Song JW. Application of instance-based entropy fuzzy support vector machine in peer-to-peer lending investment decision[J]. IEEE Access, 2019, 7: 16925-16939.