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MontageFlow: Physically Valid High Fidelity EEG Generation

arXiv 2610.14507 ↗First posted 10 Oct 2026cs.AIcited by 0

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Abstract (arXiv)

Generative modeling offers a promising solution to the scarcity of labeled clinical electroencephalography (EEG) data under strict privacy constraints. Although recent generative models report substantial improvements in generation fidelity, we show that these gains conceal a categorical failure. EEG is not an arbitrary multichannel signal: each channel records the voltage difference between two scalp electrodes. So, real recordings satisfy exact linear identities among their channels. Current generators invariably violate these identities, yet this has gone unnoticed. We therefore propose MontageFlow, a flow-matching model that enforces these identities by restricting its output to signals that satisfy them. We find that ensuring validity improves fidelity too. Furthermore, to retain the EEG characteristics that oversmoothing washes out, we also condition the model on a physiological state vector encoded from the whole EEG window. For generation, this vector is drawn from a prior fitted in latent space. Across 3 large-scale benchmarks, our model improves TS-FID by 1.6-3.6× over the closest rival while ensuring validity.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 39 references that are in the map. First posted on arXiv 10 Oct 2026.

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29 of 39 references.

References · 29

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17 more
  1. 2024Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCIcited by 327
  2. 2023Understanding Diffusion Objectives as the ELBO with Simple Data Augmentationcited by 304
  3. 2021BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG datacited by 199
  4. 2024CBraMod: A Criss-Cross Brain Foundation Model for EEG Decodingcited by 199
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  6. 2024Constrained Synthesis with Projected Diffusion Modelscited by 89
  7. 2023Reflected Diffusion Modelscited by 87
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  9. 2025REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjectscited by 80
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  15. 2023Data Augmentation for Seizure Prediction with Generative Diffusion Modelcited by 14
  16. 2024REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updatescited by 12
  17. 2026Let EEG Models Learn EEGcited by 7

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  4. 2023EEG Synthetic Data Generation Using Probabilistic Diffusion Models
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