Shanghai Sunshine Rehabilitation Center

A team writing in Computing and Informatics has proposed FSDFGL, a federated graph learning framework that folds EMG into EEG when the graph is built and then shares only structural information between clients, avoiding the accuracy loss that comes with exchanging heterogeneous features. The design targets three problems in hybrid BCI work at once: the low spatial resolution of EEG, datasets that are small and privacy-sensitive, and the uneven data distributions federated learning has to cope with. Experiments on the TAN dataset show an advantage in identifying complex motor intentions.

MVCLDG combines raw EEG and Hilbert-derived phase information with domain-alignment and contrastive-learning constraints to improve classification across unseen users. It outperformed baseline and representative domain-generalization methods on a public error-related-negativity dataset and a semantic-syntactic-violation dataset without target-domain adaptation; ablation and activation-map analyses supported the contribution and neurophysiological plausibility of its components.

A trial registered with the Chinese Clinical Trial Registry combines low-frequency rTMS with motor imagery brain-computer interface therapy, using conventional physical rehabilitation as the control, with the Fugl-Meyer upper extremity scale as the primary endpoint. The study has three parts: Part I compares BCI therapy with conventional rehabilitation, while Part III compares three rTMS protocols. It plans to enroll 213 participants, with first enrolment set for May 1, 2026; recruitment has not yet begun.