  Low-channel EEG devices are crucial for portable and entertainment
applications. However, the low spatial resolution of EEG presents challenges in
decoding low-channel motor imagery. This study introduces TSFF-Net, a novel
network architecture that integrates time-space-frequency features, effectively
compensating for the limitations of single-mode feature extraction networks
based on time-series or time-frequency modalities. TSFF-Net comprises four main
components: time-frequency representation, time-frequency feature extraction,
time-space feature extraction, and feature fusion and classification.
Time-frequency representation and feature extraction transform raw EEG signals
into time-frequency spectrograms and extract relevant features. The time-space
network processes time-series EEG trials as input and extracts temporal-spatial
features. Feature fusion employs MMD loss to constrain the distribution of
time-frequency and time-space features in the Reproducing Kernel Hilbert Space,
subsequently combining these features using a weighted fusion approach to
obtain effective time-space-frequency features. Moreover, few studies have
explored the decoding of three-channel motor imagery based on time-frequency
spectrograms. This study proposes a shallow, lightweight decoding architecture
(TSFF-img) based on time-frequency spectrograms and compares its classification
performance in low-channel motor imagery with other methods using two publicly
available datasets. Experimental results demonstrate that TSFF-Net not only
compensates for the shortcomings of single-mode feature extraction networks in
EEG decoding, but also outperforms other state-of-the-art methods. Overall,
TSFF-Net offers considerable advantages in decoding low-channel motor imagery
and provides valuable insights for algorithmically enhancing low-channel EEG
decoding.
