Electroencephalogram (EEG) based brain-computer interface (BCI) has been proved to be an effective communication way between human brain and external devices. In order to effectively recover the cortical dynamics from the EEG signals and improve the classification performance, plenty of studies focused on constructing subject-specific spatial and spectral filters, achieving considerable improvement in classification accuracy. However, almost all the approaches aimed to find one common subspace for projection of all the samples in different classes. Studies have shown that active channels and frequency information were not only subject-dependent but also class-dependent. Thus the variety of class-dependent spatial and spectral characteristics can provide further discriminative information for classification. In this paper, we proposed a tensor based method which attempted to seek individual spatial and spectral subspaces for each class by which each class was projected into its own subspace separately such that they were easily to be classified. Finally, we added a regularization term in this model to avoid overfitting. We evaluated the effectiveness and robustness of the proposed method on two different datasets including one widely-used benchmark EEG dataset collected from healthy subjects and one self-collected EEG dataset collected from stroke patients. The results demonstrated its superior performance.
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