Tianjin Medical University

Researchers released a multi-paradigm longitudinal EEG dataset from 24 stroke patients, covering a novel 'sixth-finger' motor imagery paradigm and affected-hand motor imagery. The dataset spans the full pre-training, post-training and follow-up stages and includes raw EEG, preprocessed data and patient clinical information. Preliminary analysis with classical classifiers (CSP+SVM, CSP+LDA) kept average cross-paradigm classification accuracy at roughly 85%–86%.

Researchers assessed the feasibility of a hybrid brain-computer interface that integrates motor imagery (MI) and steady-state visual evoked potentials (SSVEP) with robotic glove feedback for upper limb motor rehabilitation in 32 stroke patients, split into a conventional-treatment control group and an experimental group receiving 10- or 20-day BCI interventions. The experimental group showed considerable improvement in Fugl-Meyer scores over the control group, and the BCI achieved EEG classification accuracy up to 98.08% with stable operation; after longer training, accuracy rose, the laterality coefficient moved toward normal, and task-related brain connectivity strengthened. The authors say the hybrid system may overcome the limits of conventional therapy and single-modality BCIs.