An EEG-sEMG Asynchronous Time-Frequency Progressive Fusion Model for Hand Trajectory Estimation

Accurate motor trajectory estimation from physiological signals is essential for developing advanced motor rehabilitation and bionic devices. Fusion of electroencephalography (EEG) and surface electromyography (sEMG) leverages complementary information, yet existing methods primarily target discrete intent classification. Current studies often utilize simultaneously collected EEG and sEMG, assuming temporal alignment between these signals and thereby overlooking the inherent latency between the two modalities. This oversight induces semantic misalignment and insufficient consistency representation, ultimately degrading performance in continuous motion trajectory decoding. To overcome these limitations, this paper proposes AtpFusion, an EEG-sEMG asynchronous time-frequency progressive fusion model for enhanced 3-dimensional (3D) hand trajectory decoding. Key contributions: 1) asynchronous time-frequency inputs, constructed using a physiologically-inspired long-short time window segmentation strategy for semantic alignment, comprising long-window frequency-domain EEG (amplitude/phase) and short-window time-domain sEMG signals; and 2) a progressive hierarchical fusion architecture with intra-modal and inter-modal branches, designed for effective hierarchical feature refinement and integration for regression. AtpFusion is evaluated on the public WAY-EEG-GAL dataset, performing, to our knowledge, the first EEG-sEMG-based continuous hand trajectory estimation on this benchmark. The proposed model yields state-of-the-art accuracy with a Pearson Correlation Coefficient (PCC) of 0.9278 and a Root Mean Square Error (RMSE) of 0.0916, significantly outperforming existing approaches. This work presents a novel asynchronous EEG-sEMG fusion framework, offering a high-performance solution for practical multimodal bionic interfaces.