HCFNet: A heterogeneous frequency bands coupling CNN for enhanced short-time fast response in motor imagery decoding

Motor imagery signals encompass a broad range of frequency components, and frequency band decomposition can improve the precision of frequency-domain features, helping the model focus on task-relevant information. However, existing methods often treat signals from different frequency bands uniformly, overlooking their heterogeneity and coupling, which leads to redundant features and loss of cooperative information.

We propose a HCFNet that explores heterogeneous feature extraction and coupling across frequency bands. HCFNet first separates the raw signal into high and low-frequency bands, extracting spatiotemporal features through specialized modules. A cross-frequency coupling module then fuses these features, using data augmentation for regularization to capture robust spectral-spatiotemporal features and high-low frequency coupling.

We evaluated our model on the BCIC-IV-2a and OpenBMI benchmark datasets, and our model achieves average accuracies of 82.41 % and 76.52 %. Notably, HCFNet maintains excellent performance even with shorter time windows.

HCFNet outperforms all the state-of-the-art methods we benchmark against. Compared with traditional multi-band isomorphic methods, frequency-band heterogeneous coupling performs better in capturing task-related features and significantly reduces redundancy during feature fusion.

This study significantly advances the decoding technology of motor imagery signals through an innovative frequency-band heterogeneous coupling method. Its substantial potential for rapid responses brings tangible improvements to brain-computer interface systems and is expected to be further applied in domain adaptation, cross-domain alignment, and cross-subject contexts in the future.