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arXiv Machine Learning By Jintao Zhang, Yidan Ding, Joshua Kosnoff, Maxim Karrenbach, Hanwen Wang, Bin He

FRIST: FMRI Representation Informed Shared-space Training Improves EEG-only Individual-Finger BCI Decoding

Read the original on arXiv Machine Learning →

The paper introduces FRIST, a two‑stage EEG decoding framework that uses fMRI data to learn spectral projections and class geometry, then refines EEG predictions. In experiments with 12 participants, FRIST improves finger‑level motor decoding accuracy across both movement execution and motor imagery tasks, outperforming EEG‑only baselines and generalizing across different EEG backbones. The method demonstrates that fMRI’s spatial resolution can enhance noninvasive EEG‑based BCI performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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