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

• Main Authors: Yu Pei (Author), Zhiguo Luo (Author), Hongyu Zhao (Author), Dengke Xu (Author), Weiguo Li (Author), Ye Yan (Author), Huijiong Yan (Author), Liang Xie (Author), Minpeng Xu (Author), Erwei Yin (Author)
• Format: Book
• Published: IEEE, 2022-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1558-0210; 10.1109/TNSRE.2021.3125386
• Keywords: Brain-computer interface; electroencephalogram; motor imagery; common spatial pattern; tensor-to-vector projection; fast fourier transformation; Medical technology; R855-855.5; Therapeutics. Pharmacology; RM1-950
• Source: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 30, Pp 465-475 (2022)

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&#x0025; (<inline-formula> <tex-math notation="LaTeX">${p} &lt; 0.01$ </tex-math></inline-formula>). 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.