arXiv — cs.AI preprintsInternational7 October 2026
Sensor Geometry as a Flow-Matching Prior for Multi-Channel Brain Signals
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv: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 th
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- GAMEGO: Training Game-Dev Agents with Synthetic Trajectories Anchored in Real-World Assets7 October 2026
- Text2Dashboard: A Governed Agent Architecture for Natural-Language Dashboard Generation over Enterprise DataBrain7 October 2026
- FluidPD: In-Place Elasticity for SLO-Aware Prefill-Decode Disaggregated LLM Serving7 October 2026
- Anchor Divergence for Semantic Geometry in Contrastive Learning7 October 2026
- RadOnc-Agent: An LLM-Orchestrated Framework for AI Workflows Across the Radiotherapy Care Pathway7 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.