Selective multi–view time–frequency decomposed spatial feature matrix for motor imagery EEG classification

Decoding brain activity from non-invasive motor imagery electroencephalograph (MI-EEG) has garnered significant attentions for brain-computer interface (BCI) and brain disorders. Notably, owing to the remarkable advances in feature representation, extracting and selecting discriminative features in EEG decoding has gained widespread popularity in recent years. However, many EEG studies suffer from limited sample sizes with low signal-to-noise ratio, making it difficult to effectively represent complementary features in a single view. To address this fundamental limitation, this paper proposes a novel method to Selective extract the Multi-View Time-Frequency decomposed Spatial (S-MVTFS) feature matrix, which employs spatial features from Euclidean and Riemannian spaces based on time–frequency decompositions. It selectively extracts discriminative features on manifold embedded space and classifies feature matrix through sparse support matrix machine. The proposed method has been systematically benchmarked on three BCI competition MI-EEG datasets, and its classification performance surpasses several state-of-the-art methods. Notably, the S-MVTFS method achieved average classification improvements of 0.45%, 2.28%, and 2.04% on BCI-III dataset 4a, BCI-IV dataset 1, and BCI-IV dataset 2a, respectively. Moreover, it effectively captures meaningful temporal varying and spatially coupled features with parameter insensitivity. Our method therefore provides a novel MI-EEG tailored feature representation for decoding brain activity.