arXiv AI
CAMEL-CLIP: Channel-aware Multimodal Electroencephalography-text Alignment for Generalizable Brain Foundation Models
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arXiv AI
Sep 7
ProCA: Progressive Contrastive Alignment for Robust EEG Visual 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 AI
Aug 14
EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding
arXiv:2608. 13072v1 Announce Type: new Abstract: Electroencephalography (EEG) decoding models often generalize poorly across datasets and subjects due to domain shifts in acquisition protocols and individual neurophysiology.
arXiv Computer Vision
Oct 7
RPA: Residual Patch-Token Adapter for Image Retrieval from EEG and MEG
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.
arXiv AI
Jul 29
Joint Text-Audio Alignment for EEG-to-Text Decoding in Chinese Speech Production and Perception
arXiv:2607. 25626v1 Announce Type: new Abstract: Decoding speech information directly from scalp electroencephalography (EEG) into text provides a potential non-invasive neural communication pathway for individuals with severe speech and motor impairments.
arXiv AI
Jul 24
Multimodal Pretraining for Generalizable EEG Representation Learning
arXiv:2607. 21384v1 Announce Type: new Abstract: Electroencephalography (EEG) models used for epilepsy are often limited to specific datasets and tasks.
arXiv Machine Learning
Jul 7
NeuroOnline: Bridging Pretraining and Online Adaptation for EEG Foundation Models
arXiv:2607. 03925v1 Announce Type: new Abstract: EEG foundation models have shown strong potential in learning generalized representations across subjects and tasks.