# BrainWave: A Brain Signal Foundation Model for Clinical Applications

arXiv: https://arxiv.org/abs/2402.10251 · HTML: https://mlsanity.com/paper/2402.10251/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 71 references that are in the map.
- First posted on arXiv 15 Feb 2024.

## Builds on (most cited first, 7 of 7)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [MOMENT: A Family of Open Time-series Foundation Models](https://mlsanity.com/paper/2402.03885/) (2024)
- [BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks](https://mlsanity.com/paper/2305.17100/) (2023)
- [BrainBERT: Self-supervised representation learning for intracranial recordings](https://mlsanity.com/paper/2302.14367/) (2023)
- [Inter-database validation of a deep learning approach for automatic sleep scoring](https://mlsanity.com/paper/2009.10365/) (2020)
- [Large-scale neural recordings call for new insights to link brain and behavior](https://mlsanity.com/paper/2103.14662/) (2021)
- [BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning](https://mlsanity.com/paper/2306.13101/) (2023)

## Built on by (most cited first, 26 of 26)

- [CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2506.23075/) (2025)
- [EEG Foundation Models: A Critical Review of Current Progress and Future Directions](https://mlsanity.com/paper/2507.11783/) (2025)
- [BrainOmni: A Brain Foundation Model for Unified EEG and MEG Signals](https://mlsanity.com/paper/2505.18185/) (2025)
- [Brain Foundation Models: A Survey on Advancements in Neural Signal Processing and Brain Discovery](https://mlsanity.com/paper/2503.00580/) (2025)
- [The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning](https://mlsanity.com/paper/2406.04328/) (2024)
- [Uni-NTFM: A Unified Foundation Model for EEG Signal Representation Learning](https://mlsanity.com/paper/2509.24222/) (2025)
- [Brain4FMs: A Benchmark of Foundation Models for Electrical Brain Signal](https://mlsanity.com/paper/2602.11558/) (2026)
- [NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces](https://mlsanity.com/paper/2605.14698/) (2026)
- [Dual-TSST: A Dual-Branch Temporal-Spectral-Spatial Transformer Model for EEG Decoding](https://mlsanity.com/paper/2409.03251/) (2024)
- [Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning](https://mlsanity.com/paper/2411.16155/) (2024)
- [MEG-GPT: A transformer-based foundation model for magnetoencephalography data](https://mlsanity.com/paper/2510.18080/) (2025)
- [BioSerenity-E1: a self-supervised EEG model for medical applications](https://mlsanity.com/paper/2503.10362/) (2025)
- [Foundation Models for Cross-Domain EEG Analysis Application: A Survey](https://mlsanity.com/paper/2508.15716/) (2025)
- [Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics](https://mlsanity.com/paper/2502.17213/) (2025)
- [Neuroprobe: Evaluating Intracranial Brain Responses to Naturalistic Stimuli](https://mlsanity.com/paper/2509.21671/) (2025)
- [ZUNA: Flexible EEG Superresolution with Position-Aware Diffusion Autoencoders](https://mlsanity.com/paper/2602.18478/) (2026)
- [Bridging Brain with Foundation Models through Self-Supervised Learning](https://mlsanity.com/paper/2506.16009/) (2025)
- [Pre-trained Transformer-models using chronic invasive electrophysiology for symptom decoding without patient-individual training](https://mlsanity.com/paper/2508.10160/) (2025)
- [From Minutes to Days: Scaling Intracranial Speech Decoding with Supervised Pretraining](https://mlsanity.com/paper/2512.15830/) (2025)
- [SpecMoE: Spectral Mixture-of-Experts Foundation Model for Cross-Species EEG Decoding](https://mlsanity.com/paper/2603.16739/) (2026)
- [MANAS-2: Constrained Reconstruction for EEG Foundation Models](https://mlsanity.com/paper/2609.13717/) (2026)
- [Brain foundation model-guided source-selective domain adaptation for cross-subject EEG decoding](https://mlsanity.com/paper/2507.21037/) (2025)
- [Beyond Hearing: Learning Task-Agnostic ExG Representations from Earphones via Physiology-Informed Tokenization](https://mlsanity.com/paper/2510.20853/) (2025)
- [RECTOR: Masked Region-Channel-Temporal Modeling for Affective and Cognitive Representation Learning](https://mlsanity.com/paper/2606.15278/) (2026)
- [ZUNA1.1: A more flexible EEG foundation model for Denoising and Super-resolution](https://mlsanity.com/paper/2607.27308/) (2026)
- [Representational and Functional Robustness to Electrode Montages in EEG Foundation Models](https://mlsanity.com/paper/2609.36288/) (2026)

## Abstract (arXiv)

Neural electrical activity is fundamental to brain function, underlying a range of cognitive and behavioral processes, including movement, perception, decision-making, and consciousness. Abnormal patterns of neural signaling often indicate the presence of underlying brain diseases. The variability among individuals, the diverse array of clinical symptoms from various brain disorders, and the limited availability of diagnostic classifications, have posed significant barriers to formulating reliable model of neural signals for diverse application contexts. Here, we present BrainWave, the first foundation model for both invasive and non-invasive neural recordings, pretrained on more than 40,000 hours of electrical brain recordings (13.79 TB of data) from approximately 16,000 individuals. Our analysis show that BrainWave outperforms all other competing models and consistently achieves state-of-the-art performance in the diagnosis and identification of neurological disorders. We also demonstrate robust capabilities of BrainWave in enabling zero-shot transfer learning across varying recording conditions and brain diseases, as well as few-shot classification without fine-tuning, suggesting that BrainWave learns highly generalizable representations of neural signals. We hence believe that open-sourcing BrainWave will facilitate a wide range of clinical applications in medicine, paving the way for AI-driven approaches to investigate brain disorders and advance neuroscience research.