arXiv Machine Learning October 8, 2026 By Parastoo Azizeddin, Omid Sharafi, Maryam M. Shanechi

Multimodal LLMs Can Learn to Read Brain Signals: A Vision--Language Model for Unified Multi-Task EEG Decoding

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 24

MSBraM: A Multi-scale Self-supervised Brain Foundation Model for Hierarchical EEG Dynamics Learning

arXiv:2607. 21402v1 Announce Type: new Abstract: Self-supervised foundation models have recently shown strong potential for electroencephalogram (EEG)-based analysis.

By Tao Zhou, Jing Han, Lingyu Shu, Zixing Zhang

nlp efficiency benchmarks

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.

By Shuailei Zhang, Muyun Jiang, Wei Zhang, Jinbo Chen, Zhiwei Guo, Yong Li, Yi Ding, Cuntai Guan

rag fine-tuning benchmarks

arXiv AI

Jun 2

EvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks

arXiv:2606. 01767v1 Announce Type: new Abstract: Electroencephalography (EEG) is the cornerstone of non-invasive brain-computer interfaces (BCIs), yet conventional decoding relies on fragmented, task-specific architectures that severely limit cross-task scalability.

By Yangxuan Zhou, Sha Zhao, Jiquan Wang, Shijian Li, Gang Pan

fine-tuning efficiency benchmarks

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.

By Targol Bakhtiarvand, Jugal Kalita, Adham Atyabi

llms rag computer-vision fine-tuning multimodal benchmarks safety

arXiv AI

Aug 26

Taming foundation model with invariance-oriented pre-training for broad-spectrum EEG analysis across signal-level, brain-state, and brain-health tasks

arXiv:2608.24597v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a widely used window into human brain function, but most EEG models remain tied to a one-dataset-one-model supervised...

By Yulong Dou, Han Wu, Guo Chen, Fangmao Ju, Zhiming Cui, Dinggang Shen

fine-tuning benchmarks

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%.

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

multimodal safety

One email a morning, machine-written

Related stories

MSBraM: A Multi-scale Self-supervised Brain Foundation Model for Hierarchical EEG Dynamics Learning

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding

EvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks

Multimodal Pretraining for Generalizable EEG Representation Learning

Taming foundation model with invariance-oriented pre-training for broad-spectrum EEG analysis across signal-level, brain-state, and brain-health tasks

ProCA: Progressive Contrastive Alignment for Robust EEG Visual Decoding