Laboratory for Neuro- and Psychophysiology

A collaboration involving West China Hospital of Sichuan University combined healthy neural recordings with biophysical constraints to decode motor signals affected by pathology. In a gesture classification task using high-density electrocorticography from patients with gliomas infiltrating the motor cortex, the study reported improved classification accuracy. Correlation between motor-command representations and healthy canonical representations increased from r = 0.66 to r = 0.94. The authors interpret the model's behavior as an "inverse weighting" strategy that downweights high-power pathological noise and redirects spatial attention toward functional compensatory regions. This is a proof of concept for decoding in compromised networks, not evidence of restored motor function.

Published in Journal of Neural Engineering on August 7, 2026, the study analyzed human electrocorticography data from four subjects to investigate medial wall contributions to finger movement decoding. Significantly above-chance finger movement detection was observed across multiple medial wall subregions, with local motor potentials and oscillatory power in the 8-12 Hz and 12-34 Hz bands contributing most strongly. Feature dynamics shared key properties with primary motor cortex, including pre-movement desynchronization, while also exhibiting region-specific positive or negative LMP modulations. Medial wall channels in two subjects enabled significant differentiation between individual fingers, and one subject showed decoding of both contralateral and ipsilateral finger movements, though this is a single case and preliminary.