Iterative Outlier Removal Clustering Based Time-Frequency-Spatial Feature Selection for Binary EEG Motor Imagery Decoding

Electroencephalography (EEG)-based motor imagery (MI) brain-computer interface (BCI) is a bridge in the instruments of rehabilitation and motor assistance field to control external assist devices without external stimulation. Feature extraction (FE) is a key to improving the performance of EEG MI decoding. Existing FE methods usually extract single-domain features such as spatial, frequency features, or dual-domain features such as time-frequency features. However, the multidomains features which describe the intent more comprehensively are not simultaneously extracted. Therefore, inspired by short-time Fourier transform (STFT) and common spatial pattern (CSP) FE methods, a frequency-spatial-temporal multidomain (FSTMD) FE method is proposed. To solve the problem of the high dimensions of the FSTMD features, a feature selection (FS) method is designed. In the proposed FSTMD FE process, the STFT is employed to extract the time-frequency domain features first. Then, the time-frequency features of each channel are cascaded. After that, the cascaded features are divided according to time segments. At last, the covariance transform is performed for each time segment. Since the proposed features have a one-to-one correspondence with frequency, channel, and time, the extracted features have good interpretability. The proposed FS method selects the features in the frequency-spatial domain of each time segment based on mutual information and correlation metrics first and then selects the key time segment based on clustering with outlier removal. The performance of the proposed feature is verified in BCI Competition IV Dataset IIa and IIb. Compared to the CSP feature in the only spatial domain, the proposed feature has significantly lower deviation and slightly higher accuracy. The average recognition accuracy of the proposed MI decoding framework is 85.1% on the dataset IIb session 3, which is competitive compared with the state-of-the-art methods.