Single-trial discrimination of EEG signals for stroke patients: a general multi-way analysis

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.