BCIwiki (bciwiki.com) — A preprint proposes using fMRI-derived representation information to constrain an EEG decoding model, letting a non-invasive brain-computer interface tell individual fingers apart without adding electrodes. The work was posted to the arXiv preprint server on September 11, 2026, and has not been peer reviewed. The framework, called FRIST (fMRI Representation-Informed Shared-Space Training), works in two stages: it first learns fMRI-informed spectral projections from simultaneously recorded EEG and fMRI, then uses fMRI-derived class geometry to guide residual refinement of EEG predictions.
Finger representations sit close together in the sensorimotor cortex, and volume conduction blurs the signal, making individual-finger decoding from scalp EEG difficult. FRIST transfers information across recordings through shared finger labels without requiring paired trials, and uses only EEG at inference. The study enrolled 12 able-bodied participants and evaluated two-class and three-class chronological session-held-out decoding during movement execution and motor imagery, simulating an online scenario.
With EEGNet as the EEG feature extractor, FRIST raised group average accuracy from 66.93% to 74.53% for two-class movement execution, from 44.83% to 56.58% for three-class movement execution, from 80.78% to 85.63% for two-class motor imagery, and from 60.93% to 69.90% for three-class motor imagery. The method also improved EEG-only decoding when the target participant's own fMRI data were unavailable, and generalized across decoding backbones, reaching 87.40% for two-class motor imagery and 72.54% for three-class motor imagery with EEG Conformer. The authors conclude that fMRI provides useful spatial constraints for EEG representation learning, offering a multimodal strategy that combines fMRI's spatial specificity with EEG's real-time applicability.