by brain-bzh
Open source · 30k downloads · 22 likes
REVE-base is a language model specialized in processing EEG signals, built on a transformer architecture. Trained on over 60,000 hours of EEG data from 92 datasets and 25,000 subjects, it stands out for its ability to adapt to different electrode configurations and a wide range of EEG-related tasks. Its innovative 4D positional encoding enables it to handle signals of varying lengths and arrangements, providing unprecedented flexibility. This model is particularly valuable for medical, neuroscientific, or brain-computer interface applications where EEG data analysis is critical. Developed by the BRAIN team and the University of Montreal, REVE-base is positioned as a powerful tool for extracting versatile and generalizable EEG representations.
REVE (project page here) is a transformer-based foundation model for EEG signal processing. It was trained on 60k hours of EEG data from various sources and is designed to be adaptable to any electrode configuration and a wide range of EEG-based tasks.
REVE (Representation for EEG with Versatile Embeddings), a pretrained encoder 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.
Developed by the BRAIN team and UdeM
Funded by: This research was supported by the French National Research Agency (ANR) through its AI@IMT program and grant ANR-24-CE23-7365, as well as by a grant from the Brittany region. Further support was provided by a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada (NSERC), by funding from the Canada Research Chairs program and the Fonds de recherche du Québec – Nature et technologies (FRQ-NT). This work was granted access to the HPC resources of IDRIS under the allocation 2024-AD011015237R1 made by GENCI, as well as HPC provided by Digital Alliance Canada.
Example script to extract embeddings with REVE, using our position bank:
from transformers import AutoModel
pos_bank = AutoModel.from_pretrained("brain-bzh/reve-positions", trust_remote_code=True)
model = AutoModel.from_pretrained("brain-bzh/reve-base", trust_remote_code=True)
eeg_data = ... # EEG data as a torch Tensor (batch_size, channels, time_points), must be sampled at 200 Hz
electrode_names = [...] # List of electrode names corresponding to the channels in eeg_data
positions = pos_bank(electrode_names) # Get positions (channels, 3)
# Expand the positions vector to match the batch size
positions = positions.expand(eeg_data.size(0), -1, -1) # (batch_size, channels, 3)
output = model(eeg_data, positions)