Tensor Representation for Brain Signal Processing (Extended Abstract)

In the past decades, electroencephalograms (EEG) has been widely used for brain-computer interface (BCI). To detect the pattern from EEG signals, different kinds of algorithms have been developed. In our previous works, we already have shown that tensor presentation of EEG signals can improve the EEG signal classification. And the tensor fusion algorithm was used for a multi-modal BCI system. In the system, electroencephalography and near-infrared spectroscopy were recorded simultaneously, tensor-based fused feature vector was used for the classification. The results showed better performance than previous researches.