AI RESEARCH
Visual Neural Decoding via Improved Visual-EEG Semantic Consistency
arXiv CS.CV
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ArXi:2408.06788v2 Announce Type: replace Visual neural decoding aims to extract and interpret original visual experiences directly from human brain activity. Recent studies have nstrated the feasibility of decoding visual semantic categories from electroencephalography (EEG) signals, among which metric learning-based approaches have delivered promising results. However, these methods that directly map EEG features into a pre-trained embedding space inevitably