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About this paper
Guoan Wang, Feng Liu
SeeEEG adapts EEG displays for a frozen image-pretrained vision transformer, arranging each channel in a row of image patches while fitting a task-specific display and readout head. Across eight benchmarks, frozen DINOv3 SeeEEG exceeds seven EEG foundation-model baselines in balanced accuracy; on TUSL slowing classification it scores 90.4% versus REVE’s 74.5%. This is not zero-shot classification or evidence of clinical readiness.
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