DenoFlow: Flow Matching for SSVEP Denoising under Real Physiological Artifacts

DenoFlow introduces a flow‑matching approach to denoise steady‑state visual evoked potential (SSVEP) EEG signals, avoiding direct mapping from noisy to clean data. It uses a field network that predicts the velocity of a straight path between contaminated and clean trials, integrating this field to produce denoised outputs while simultaneously training a classifier to supervise the result. Experiments on two public SSVEP datasets demonstrate that DenoFlow surpasses seven baseline denoising models in both signal fidelity and downstream decoding accuracy.

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