brain-bzh/reve-base
feature-extraction model by brain-bzh
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Model card & documentation
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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…
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Model overview
brain-bzh/reve-base is a feature-extraction model repository published by brain-bzh on Hugging Face. The source reports the transformers library.
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Model facts
- Task
- feature-extraction
- Library
- transformers
- Recent downloads (30 days)
- 88,890
- Cumulative likes
- 58
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 69,189,632
- Architecture
- Reve
- License
- other
- Access
- Not gated by Hugging Face
- Created
- 2025-11-26T11:40:41.000Z
- Last modified
- 2026-11-03T16:17:59.000Z
Compatibility and lineage
Language coverage was not reported.
Base-model lineage was not included.
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-06T06:56:51.347Z. Daily imports are snapshots, not real-time monitoring.
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