REVE: A Foundation Model for EEG
Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects

Yassine El Ouahidi1 Jonathan Lys1 Philipp Thölke2 Nicolas Farrugia1
Bastien Pasdeloup1 Vincent Gripon1 Karim Jerbi2 Giulia Lioi1
1IMT Atlantique 2Université de Montréal
NeurIPS 2025
Paper Supplementary Code arXiv
REVE Model Overview

Overview of the REVE architecture and training pipeline.

Abstract

Foundation models have transformed AI by reducing reliance on task-specific data through large-scale pretraining. While successful in language and vision, their adoption in EEG has lagged due to the heterogeneity of public datasets, which are collected under varying protocols, devices, and electrode configurations. Existing EEG foundation models struggle to generalize across these variations, often restricting pretraining to a single setup, resulting in suboptimal performance, in particular under linear probing. We present REVE (Representation for EEG with Versatile Embeddings), a pretrained model explicitly designed to generalize across diverse EEG signals. REVE introduces a novel 4D positional encoding scheme that enables it to process signals of arbitrary length and electrode arrangement. Using a masked autoencoding objective, we pretrain REVE on over 60,000 hours of EEG data from 92 datasets spanning 25,000 subjects, representing the largest EEG pretraining effort to date. REVE achieves state-of-the-art results on 10 downstream EEG tasks, including motor imagery classification, seizure detection, sleep staging, cognitive load estimation, and emotion recognition. With little to no fine-tuning, it demonstrates strong generalization, and nuanced spatio-temporal modeling.

Code example

from transformers import AutoModel

model = AutoModel.from_pretrained("brain-bzh/reve-base", trust_remote_code=True)
pos_bank = AutoModel.from_pretrained("brain-bzh/reve-positions", trust_remote_code=True)

eeg_data = ...  # EEG data (batch_size, channels, time_points)
positions = pos_bank(montage, electrode_names) # Get positions (batch_size, channels, 3)
output = model(eeg_data, positions)

Dataset Composition (Proportion of Total Hours)

Data Sources

Channel Counts

Tables

BibTeX

@article{el2025reve,
  title={REVE: A Foundation Model for EEG - Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects},
  author={El Ouahidi, Yassine and Lys, Jonathan and Thölke, Philipp and Farrugia, Nicolas and Pasdeloup, Bastien and Gripon, Vincent and Jerbi, Karim and Lioi, Giulia},
  journal={NeurIPS},
  year={2025},
  url={https://neurips.cc/virtual/2025/poster/117334}
}