SYNAPSE: Neuro-Symbolic Visual Thought-to-Text Decoding via Topological Semantic Denoising
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
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:2606. 30319v1 Announce Type: cross Abstract: Modeling the bidirectional correspondence between external sensory stimuli and internal neural activity has emerged as a critical frontier in neuroscience.
arXiv:2603. 03312v3 Announce Type: replace-cross Abstract: Decoding natural language from non-invasive EEG signals is a promising yet challenging task.
arXiv:2503. 06437v3 Announce Type: replace-cross Abstract: We present SEED (Semantic Evaluation for Visual Brain Decoding), a novel metric for evaluating the semantic decoding performance of visual brain decoding models.
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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...