Brain–computer interface–based neurofeedback training enables transferable control of cortical state switching in humans - Seitaro Iwama; Atsuya Matsuoka; Junichi Ushiba (Proceedings of the National Academy of Sciences, 2026)
Behavioral flexibility relies on transient neural dynamics that govern cortical state transitions. However, whether humans can deliberately learn to control such state transitions and generalize trained neural dynamics beyond contexts remains unclear. Here, we demonstrate that operation of a brain–computer interface (BCI) which links time evolution of sensorimotor activity with real-time feedback enables volitional control over the targeted neural population. Compared with a double-blind sham control group, trained participants modulated sensorimotor oscillations in the absence of BCI. Data-driven latent-state analysis further revealed stronger interregional phase coupling and steeper broadband spectral slope in the medial frontal cortex during transitions. The training-induced reorganization of sensorimotor dynamics was found during movement execution and associated with performance improvement, indexed by reduced reaction times for both muscle contraction and relaxation. These findings provide evidence that learned control over cortical state transitions enhances behavioral flexibility beyond the training context.