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arXiv AI By Shaocong Wang, Tong Liu, Yihan Li, Ming Li, Kairui Wen, Pei Yang, Wenqi Ji, Minjing Yu, Yong-Jin Liu

EEGDM: Learning EEG Representation with Latent Diffusion Model

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EEGDM introduces a self‑supervised framework that uses latent diffusion models to generate EEG signals, moving beyond traditional masked reconstruction. The method employs an EEG encoder to produce a compact representation that conditions the diffusion denoising process, allowing joint optimization of encoder and generator. Experiments demonstrate that EEGDM can reconstruct high‑quality EEG, learn robust representations, and perform competitively on various downstream tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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