arXiv:2607. 18344v2 Announce Type: replace-cross Abstract: EEG-based visual decoding provides a non-invasive pathway for interpreting visual semantics.
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arXiv:2607. 18344v1 Announce Type: cross Abstract: EEG-based visual decoding provides a non-invasive pathway for interpreting visual semantics.
ProCA: Progressive Contrastive Alignment for Robust EEG Visual Decoding introduces a model‑agnostic framework that adaptively aligns EEG signals with visual semantics. It replaces fixed visual or textual anchors with EEG‑aware class‑level contrastive supervision and employs structure‑consistent interpolation to preserve channel‑wise and temporal importance. Across multiple evaluation settings—including subject‑dependent, subject‑independent, strict cross‑subject transfer, and continual adaptation—ProCA delivers significant performance gains, achieving relative Top‑1 improvements ranging from 7.4% to 28.1%.
arXiv:2605.23137v3 Announce Type: replace-cross Abstract: Electroencephalography (EEG) visual decoding remains challenging due to the modality gap between low-SNR neural signals and highly structured...
The paper introduces an adaptive cortically constrained method for aligning EEG signals with visual representations in zero‑shot brain‑to‑image retrieval. It reconstructs EEG into ROI‑level source patterns, encodes them with a Neuro‑ROI Attention Encoder, and applies evidence‑based adaptive visual supervision to account for response‑wise variability. On the THINGS‑EEG dataset, the approach achieves strong 200‑way retrieval performance and offers ROI‑level attribution for interpretability.
arXiv:2601.21948v2 Announce Type: replace Abstract: Neural visual decoding is a central problem in brain-computer interface research, aiming to reconstruct human visual perception and to elucidate th...
arXiv:2607. 12364v1 Announce Type: cross Abstract: EEG-to-image evaluation should distinguish visual fidelity from recoverable meaning.