Sicheng Dai, Kai Chen, Hongwang Xiao, Shan Yu, Qiwei Ye

Researchers propose MTEEG, a multi-task learning framework that adapts pre-trained EEG models to multiple downstream tasks simultaneously using task-specific low-rank adaptation modules. The approach addresses a core challenge in brain-computer interface development: EEG signals vary significantly across subjects, devices, and experimental setups, creating task conflicts that degrade joint optimization. Testing across six downstream tasks, MTEEG outperforms single-task baselines on most metrics while reducing computational and storage overhead compared to maintaining separate models for each task.