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This paper argues that current EEG generative models violate the linear relationships between channels that real recordings obey, and proposes a flow-matching model that constrains its outputs to satisfy them. The authors report that enforcing validity also improves fidelity, and they claim a 1.6-3.6x TS-FID gain on three benchmarks. For ML readers, the useful takeaway is the general principle of building known physical constraints into a generator's output space, though the claims rest on the authors' benchmarks and metrics, which are worth checking before generalizing.
MontageFlow: Physically Valid High Fidelity EEG Generation
arXiv:2610.14507v1 Announce Type: new Abstract: 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$\times$ over the closest rival while ensuring validity.
Abstract: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$\times$ over the closest rival while ensuring validity.
Artificial Intelligence (cs.AI)
arXiv:2610.14507 [cs.AI]
(or arXiv:2610.14507v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.14507 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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