A Tensor-Based Frequency Features Combination Method for Brain-Computer Interfaces

With the development of the brain-computer interface (BCI) community, motor imagery-based BCI system using electroencephalogram (EEG) has attracted increasing attention because of its portability and low cost. Concerning the multi-channel EEG, the frequency component is one of the most critical features. However, insufficient extraction hinders the development and application of MI-BCIs. To deeply mine the frequency information, we proposed a method called tensor-based frequency feature combination (TFFC). It combined tensor-to-vector projection (TVP), fast fourier transform (FFT), common spatial pattern (CSP) and feature fusion to construct a new feature set. With two datasets, we used different classifiers to compare TFFC with the state-of-the-art feature extraction methods. The experimental results showed that our proposed TFFC could robustly improve the classification accuracy of about 5% (p < 0.01). Moreover, visualization analysis implied that the TFFC was a generalization of CSP and Filter Bank CSP (FBCSP). Also, a complementarity between weighted narrowband features (wNBFs) and broadband features (BBFs) was observed from the averaged fusion ratio. This article certificates the importance of frequency information in the MI-BCI system and provides a new direction for designing a feature set of MI-EEG.

Automated summary

This study proposes a method called tensor-based frequency feature combination (TFFC) to extract frequency information from electroencephalogram (EEG) and improve the classification accuracy of motor imagery-based brain-computer interface (MI-BCI) system. The experimental results show that the proposed TFFC method can consistently improve the classification accuracy and has the generalization properties of CSP and FBCSP. Additionally, a complementary relationship between weighted narrowband features and broadband features is observed.