arXiv AI By Kanglei Zhou, Chunyan Lan, Dongyang Li, Jun Zhu, Liyuan Wang

ProCA: Progressive Contrastive Alignment for Robust EEG Visual Decoding

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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%.

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arXiv AI
Oct 16

Neural Visual Decoding via Cognitive guided Adaptive Blurring and Information Constrained Alignment

The paper introduces CAIA, a framework that enhances EEG-based visual decoding by adaptively blurring visual features to match neural granularity and applying an information-constrained alignment on EEG signals. It incorporates cognitive-guided adaptive blurring, spectral sparsity regularization, and a distribution-aware boundary calibration loss to improve signal-to-noise ratio and alignment robustness. Experiments show CAIA outperforms previous methods in both subject-dependent and subject-independent zero-shot brain-to-image retrieval tasks.

By Fan Yin, Chuhang Zheng, Peiliang Gong, Donghai Guan, Qi Zhu