arXiv:2605.05556v2 Announce Type: replace Abstract: Artificial neural networks trained on visual tasks develop internal representations resembling those of the primate visual system, a discovery that...
The paper introduces the Residual Patch Adapter (RPA), a lightweight module that aligns EEG and MEG signals with all patch tokens from an intermediate layer of a Vision Transformer, rather than a single global embedding. Ablation studies show that retaining the full set of patch tokens is crucial for effective alignment, while the CLS token adds little value. Using RPA, the authors achieve state‑of‑the‑art Top‑1 accuracies on THINGS‑EEG2 and THINGS‑MEG datasets, and demonstrate that the approach generalizes to other brain encoders and can be swapped with various neural network backbones.
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.
arXiv:2606. 00121v1 Announce Type: cross Abstract: Reconstructing visual stimuli from brain recordings has been a meaningful and challenging task in brain decoding.
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. 15989v1 Announce Type: cross Abstract: Aligning neural activity across subjects offers the promise of discovering shared computational principles and generalizable decoders.
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.
arXiv:2608. 02070v1 Announce Type: cross Abstract: Brain-computer interfaces (BCIs) have been widely used in motor rehabilitation, disease diagnosis, and other neural engineering scenarios.
arXiv:2606. 16799v1 Announce Type: cross Abstract: Existing vision-language model (VLM)-based AI-generated image quality assessment (AIGIQA) methods suffer from a fundamental semantic-distortion dimensional conflict: monolithic representations optimized for semantic discrimination inherently entangle compositional understanding with low-level perceptual sensitivity, rendering them blind to fine-grained quality degradations.
arXiv:2608. 02070v2 Announce Type: replace-cross Abstract: Brain-computer interfaces (BCIs) have been widely used in motor rehabilitation, disease diagnosis, and other neural engineering scenarios.
arXiv:2607. 18344v1 Announce Type: cross Abstract: EEG-based visual decoding provides a non-invasive pathway for interpreting visual semantics.
arXiv:2605. 08245v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) increasingly power high-stakes applications, from medical imaging to autonomous systems, yet they routinely hallucinate, confidently describing content not present in the input.