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

Background 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. New method 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. Results 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. Comparison with existing methods 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. Conclusions 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.