Tianjin University Team Cuts EEG Channels Without Losing Decoding Accuracy
A team at Tianjin University in northern China has built a graph neural network framework that jointly optimizes EEG channel selection and classification, picking a small subset of channels for motor imagery decoding while holding accuracy close to that of the full electrode set. Reported in the journal Chaos, the method was validated on three datasets — BCI Competition IV 2a, High Gamma and a newly collected set — and could cut system complexity for uses such as neurorehabilitation.