Multi-Task EEG Diffusion Framework for Motor Functional Recovery in Stroke Patients

Stroke is one of the leading causes of long-term motor disability worldwide, placing a substantial burden on individuals, families, and healthcare systems. Innovative rehabilitation strategies such as motor imagery-based brain-computer interface (MI-BCI) are critical to accelerating stroke recovery. However, current MI-BCI methods face key challenges: low generalizability due to cross-patient variability, lack of effective functional assessment, limited availability of patient data, coupled with the lack of effective data augmentation approaches. To address these issues, we propose a unified EEG-based framework that simultaneously performs motor imagery classification, hemiplegic side detection, and functional recovery prediction. Our method introduces a diffusion model tailored to the spatio-temporal characteristics of EEG, incorporating a decoupled neural architecture with rotary spatial encoding and autoregressive temporal fusion. To mitigate data scarcity, we design two augmentation strategies specifically adapted to the characteristics of stroke EEG. Extensive experiments demonstrate superior performance and generalizability across multiple MI-BCI tasks, supporting the potential of the method for deployment in personalized stroke rehabilitation.