Improve the Performance of Lower Limb MI-BCI System Based on SSSEP and its Multi-Dimensional EEG Feature Analysis
Zhang Lixin1,2, Chang Meirong1, Wang Zhongpeng2, Chen Long1*, Ming Dong1,2#
1(Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China) 2(College of Precision Instrumental and Optoelectronic Engineering, Tianjin University, Tianjin 300072, China)
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.
作者简介: #中国生物医学工程学会会员(Member, Chinese Society of Biomedical Engineering)
引用本文:
张力新, 常美榕, 王仲朋, 陈龙, 明东. SSSEP提升下肢MI-BCI系统性能及其多维脑电特征分析[J]. 中国生物医学工程学报, 2021, 40(4): 429-437.
Zhang Lixin, Chang Meirong, Wang Zhongpeng, Chen Long, Ming Dong. Improve the Performance of Lower Limb MI-BCI System Based on SSSEP and its Multi-Dimensional EEG Feature Analysis. Chinese Journal of Biomedical Engineering, 2021, 40(4): 429-437.
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