Author

M. Karrenbach

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#machine learning Preprint Sep 2026

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

Finger-level motor decoding is important for naturalistic brain-computer interface (BCI) control, yet individual-finger decoding from scalp electroencephalography (EEG) remains challenging because finger representations are spatially close in the sensorimotor cortex and blurred by volume conduction. Leveraging the high...

Jintao Zhang, Yidan Ding, Joshua Kosnoff et al. · 0 citations

Open access Jul 2026

Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control

A novel sensory-guided joint learning framework that coordinates subject learning and decoder adaptation to improve BCI acquisition and advances BCI training from passive calibration to active human–machine joint learning, enabling practical and scalable neural interfaces for communication and rehabilitation.

Hanwen Wang, Yisha Zhang, M. Karrenbach et al. · Nature Communications · 0 citations