Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 28; no. 10; pp. 2153 - 2163
• Main Authors: Jin, Jing, Liu, Chang, Daly, Ian, Miao, Yangyang, Li, Shurui, Wang, Xingyu, Cichocki, Andrzej
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: bispectrum analysis; Brain; Brain-computer interface; Brain-computer interfaces; Channel estimation; channel selection; Competition; Computer applications; Computer science; Data mining; Datasets; EEG; electroencephalogram (EEG); Electroencephalography; Feature extraction; Frequency-domain analysis; Human-computer interface; Imagery; Implants; Mental task performance; motor imagery; Motor task performance
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2020.3020975

The performance of motor imagery (MI) based Brain-computer interfacing (BCI) is easily affected by noise and redundant information that exists in the multi-channel electroencephalogram (EEG). To solve this problem, many temporal and spatial feature based channel selection methods have been proposed. However, temporal and spatial features do not accurately reflect changes in the power of the oscillatory EEG. Thus, spectral features of MI-related EEG signals may be useful for channel selection. Bispectrum analysis is a technique developed for extracting non-linear and non-Gaussian information from non-linear and non-Gaussian signals. The features extracted from bispectrum analysis can provide frequency domain information about the EEG. Therefore, in this study, we propose a bispectrum-based channel selection (BCS) method for MI-based BCI. The proposed method uses the sum of logarithmic amplitudes (SLA) and the first order spectral moment (FOSM) features extracted from bispectrum analysis to select EEG channels without redundant information. Three public BCI competition datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) were used to validate the effectiveness of our proposed method. The results indicate that our BCS method outperforms use of all channels (83.8% vs 69.4%, 86.3% vs 82.9% and 77.8% vs 68.2%, respectively). Furthermore, compared to the other state-of-the-art methods, our BCS method also can achieve significantly better classification accuracies for MI-based BCI (Wilcoxon signed test, p <; 0.05).