Sensor Geometry as a Flow-Matching Prior for Multi-Channel Brain Signals
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arXiv:2610.07824v1 Announce Type: new Abstract: Electrophysiological source imaging (ESI) aims to estimate cortical source activity from noninvasive electrophysiological measurements such as electroe...
Electrophysiological source imaging (ESI) aims to estimate cortical source activity from noninvasive electrophysiological measurements such as electroencephalogram (EEG). However, ESI is fundamentally...
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.
Forecasting time-varying functional connectivity from electroencephalography (EEG) requires modeling both history-dependent trends and structured variability across channels. Conditional flow matching...
arXiv:2610.07229v1 Announce Type: new Abstract: Motivated by sequence-to-sequence transport in the context time-series domain adaptation, we study the problem of transportation between trajectories o...
arXiv:2606. 11415v1 Announce Type: cross Abstract: Neural recordings are often interpreted as local measurements, yet the signal at any one sensor can also reflect structured activity distributed across the broader network.