Slice Oriented Tensor Decomposition of EEG Data for Feature Extraction in Space, Frequency and Time Domains

In this paper we apply a novel tensor decomposition model of SOD (slice oriented decomposition) to extract slice features from the multichannel time-frequency representation of EEG signals measured for MI (motor imagery) tasks in application to BCI (brain computer interface). The advantages of the SOD based feature extraction approach lie in its capability to obtain slice matrix components across the space, time and frequency domains and the discriminative features across different classes without any prior knowledge of the discriminative frequency bands. Furthermore, the combination of horizontal, lateral and frontal slice features makes our method more robust for the outlier problem. The experiment results demonstrate the effectiveness of our method.