Fudan University

Researchers have proposed a unified EEG-based framework that simultaneously performs motor imagery classification, hemiplegic side detection and functional recovery prediction, aimed at motor rehabilitation after stroke. Stroke remains one of the leading causes of long-term motor disability worldwide, the authors note, and motor imagery brain-computer interfaces are seen as a way to accelerate recovery. Current MI-BCI methods, however, generalize poorly across patients, lack an effective functional assessment step, and are limited by scarce patient data and a shortage of suitable augmentation approaches. The framework introduces a diffusion model tailored to the spatio-temporal characteristics of EEG, built on a decoupled neural architecture with rotary spatial encoding and autoregressive temporal fusion. To offset data scarcity, the team designed two augmentation strategies adapted to stroke EEG. Experiments across multiple MI-BCI tasks show superior performance and generalizability, the authors say, supporting the method's potential for personalized stroke rehabilitation.

Researchers have built Ghost-LENet, a lightweight convolutional network that classifies motor imagery EEG at 82.18% on the BCI Competition IV-2a dataset and 83.05% on IV-2b using only a few thousand trainable parameters. The design combines dilated temporal convolutions, a stationary wavelet transform, dynamic residual fusion and Ghost modules, holding accuracy while cutting model complexity for BCI hardware with little compute to spare.

Intracranial recordings from ten epilepsy patients show that neural activity during cued limb motor imagery changes with the phase of the task: low-frequency (8-30 Hz) activity was mostly suppressed during preparation and switched to activation once imagery began, while high-frequency (60-115 Hz) responses were stronger, more widely distributed, and at some contacts followed an activation-then-suppression sequence. The stereoelectroencephalography (sEEG) data indicate that responses are not uniform but shift with task stage and brain region, which the authors tie to stage-aware feature design for BCIs and to neurorehabilitation.