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arXiv Machine Learning By Ying Zhou, Mengting Liu

GRAFT-EEG: Gauge-Aware Physical Field Transport for Cross-Dataset Motor-Imagery EEG

Read the original on arXiv Machine Learning →

The paper introduces GRAFT-EEG, a method that removes the active‑set common‑reference gauge and transports each native EEG mask through a template motor leadfield, enabling target‑statistics‑blind transfer for cross‑dataset motor‑imagery decoding. Using a source‑fitted spectral classifier and a bounded state‑space‑model residual, the approach achieves higher balanced accuracy (60.865%) than a strict‑clean baseline (52.684%) across five leave‑one‑dataset‑out targets, and outperforms several control conditions in post‑hoc tests. The study also reports positive results in a separate 60‑participant same‑paradigm evaluation, though the dynamic‑static comparison was inconclusive. "whyItMatters":"The method demonstrates that accounting for observation‑operator changes can substantially improve cross‑dataset EEG decoding performance, offering a more robust approach for motor‑imagery applications."

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