A CNN adapter plus the LaBraM foundation model achieves state-of-the-art EEG classification, 99.33% on EEG-ImageNet and 92.31% on BrainLat, with 98.21% on six unseen EEG-ImageNet classes.
Noninvasive Electroencephalo- gram Based Control of a Robotic Arm for Reach and Grasp Tasks
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EAD: An EEG Adapter for Automated Classification
A CNN adapter plus the LaBraM foundation model achieves state-of-the-art EEG classification, 99.33% on EEG-ImageNet and 92.31% on BrainLat, with 98.21% on six unseen EEG-ImageNet classes.