Sensor Geometry as a Flow-Matching Prior for Multi-Channel Brain Signals

תקציר מקורי באנגליתarXiv:2610.08355v1 Announce Type: cross Abstract: Flow-matching models start from an isotropic Gaussian source, the standard choice when the correlation structure of the data is unknown in advance. For multi-channel brain recordings, however, part of this structure is known in advance. Electrodes sit at fixed positions on the head, and volume conduction through the skull and scalp makes nearby electrodes co-vary in a way that is shared across subjects. Existing EEG generative models nonetheless leave the network to learn this from scratch. We put this structure into the source instead. From the sensor coordinates alone, we build a k-nearest-neighbor graph and take a graph-Mat\'ern function of its Laplacian as the source covariance, so the flow starts from spatially coherent patterns rather