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Single-trial discrimination of EEG signals for stroke patients: A general multi-way analysis 会议论文

编号：

aa04d6d8-e382-482f-b5a7-be4aa4f3d17a
作者：

Liu, Ye;Li, Mingfen(李明芬);Zhang, Hao;Li, Junhua;Jia, Jie(贾杰);Wu, Yi;Cao, Jianting;Zhang, Liqing
作者单位：

(1) MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, China (2) Department of Rehabilitation, Huashan Hospital, Fudan University, Shanghai, 200240, China (3) Saitama Institute of Technology, Japan
会议名称：

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
会议时间：
会议地点：

Osaka, Japan
出版信息：

2013 年 (2204 - 2207)
摘要：

It has been demonstrated that Brain-Computer Interface (BCI), combined with Functional Electrical Stimulation (FES), is an effective and efficient way for post-stroke patients to restore motor function. However, traditional feature extraction methods, such as Common Spatial Pattern (CSP), do not work well for post-stroke patients' EEG data due to its irregular patterns. In this study, we introduce a novel tensorbased feature extraction algorithm, which takes both spatial-spectral-temporal features of EEG data into consideration. EEG data recorded from post-stroke patients is used for simulation experiments to assess the effectiveness of the proposed algorithm. The results show that the the proposed algorithm outperforms some traditional algorithms. © 2013 IEEE.
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