arXiv AI
EEGaze: Multimodal Attention Prediction in Visual Search via EEG-Based Saliency Refinement
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arXiv Machine Learning
Jun 2
OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models
arXiv:2606. 00815v1 Announce Type: new Abstract: Electroencephalography (EEG) supports a variety of brain-computer interface (BCI) tasks ranging from brain-state monitoring to human-LLM interactions.
arXiv Machine Learning
Oct 8
Multimodal LLMs Can Learn to Read Brain Signals: A Vision--Language Model for Unified Multi-Task EEG Decoding
The paper introduces BraVista, a visual-language framework that transforms multichannel EEG signals into structured images and uses instruction‑conditioned vision‑language models for unified multi‑task EEG decoding. By continuing post‑training of a general‑domain VLM, BraVista adapts to neural signals without large‑scale EEG‑specific pretraining, achieving strong performance across sleep staging, emotion recognition, cognitive workload classification, and abnormal EEG detection. Analyses show that the EEG‑to‑image representation is crucial and that the model’s performance degrades gradually with added noise, indicating reliance on genuine EEG information.
arXiv AI
Jul 23
FSDBN: Foreground-Aware EEG-Visual Alignment via Dynamic Brain Networks
arXiv:2607. 18344v2 Announce Type: replace-cross Abstract: EEG-based visual decoding provides a non-invasive pathway for interpreting visual semantics.
arXiv AI
Jul 22
FSDBN: Foreground-Aware EEG--Visual Alignment via Dynamic Brain Networks
arXiv:2607. 18344v1 Announce Type: cross Abstract: EEG-based visual decoding provides a non-invasive pathway for interpreting visual semantics.
arXiv Machine Learning
Oct 28
OneEEG: A Unified Signal-Visual Multimodal Language Model with Task-Aware Mixture of Projectors for Multi-Task EEG Understanding
OneEEG is a unified signal‑visual multimodal language model designed for multi‑task EEG understanding. It jointly learns from numerical EEG data and visual EEG renderings, employing a task‑aware Mixture of Projectors to align diverse EEG features with a large language model’s embedding space. The authors introduce the OneEEG‑Instruct dataset for instruction tuning and the OneEEG‑Bench benchmark to evaluate performance, demonstrating that existing multimodal LLMs struggle with EEG inputs while OneEEG outperforms them across various tasks.
arXiv Machine Learning
Sep 16
Subject-Specific Analysis of Self-Initiated Attention Shifts from EEG with Controlled Internal and External Attention Conditions
arXiv:2605.18251v2 Announce Type: replace-cross Abstract: Self-initiated attention shifts play a critical role in voluntary behavior but are difficult to study due to the absence of explicit temporal...