Adaptive Time–Frequency Segment Optimization for Motor Imagery Classification

Abstract

Motor imagery (MI)-based brain–computer interface (BCI) has emerged as a crucial method for rehabilitating stroke patients. However, the variability in the time–frequency distribution of MI-electroencephalography (EEG) among individuals limits the generalizability of algorithms that rely on non-customized time–frequency segments. In this study, we propose a novel method for optimizing time–frequency segments of MI-EEG using the sparrow search algorithm (SSA). Additionally, we apply a correlation-based channel selection (CCS) method that considers the correlation coefficient of features between each pair of EEG channels. Subsequently, we utilize a regularized common spatial pattern method to extract effective features. Finally, a support vector machine is employed for signal classification. The results on three BCI datasets confirmed that our algorithm achieved better accuracy (99.11% vs. 94.00% for BCI Competition III Dataset IIIa, 87.70% vs. 81.10% for Chinese Academy of Medical Sciences dataset, and 87.94% vs. 81.97% for BCI Competition IV Dataset 1) compared to algorithms with non-customized time–frequency segments. Our proposed algorithm enables adaptive optimization of EEG time–frequency segments, which is crucial for the development of clinically effective motor rehabilitation.