Non-invasive neural decoding seeks to infer visual stimuli from brain recordings, such as EEG and MEG, thereby providing a computational approach for probing human visual information processing. Current contrastive learning approaches align brain signals with image embeddings from pretrained neural networks. However, t...
This work introduced Dynamic Cognitive Prior Generation (DCPG), a new framework, which can be learned and is prototype based to adaptively generate priors instead of heuristic augmentations, and can be used to significantly increase the accuracy of inter-subject retrieval on the THINGS-EEG dataset with an accuracy impr...
By aligning EEG with layer-wise neural visibility rather than fixed high-level semantics, the proposed framework improves both retrieval accuracy and image reconstruction in EEG-based visual decoding.
BrainFocus is proposed, a reliable EEG-guided efficient VLM framework for VQA that improves VQA accuracy and demonstrates that EEG can guide efficient VLM inference even when its semantic decoding is imperfect.
Brain-to-image retrieval seeks to identify the visual stimulus that elicited a non-invasive neural response. Candidate images are typically represented by pretrained vision models, whose internal representations vary in abstraction across depth. Existing methods usually train the neural encoder to recover a fixed final...
Electroencephalogram (EEG) visual decoding aims to recover visual semantics from non-invasive neural time-series signals, for which robust alignment between noisy neural responses and stable semantic representations is key to achieving high-performance decoding. Despite recent advances in contrastive learning, robust E...
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