Electroencephalography signatures of motor error and stimulus-driven attention in electrical muscle stimulation-induced wrist movements under motor imagery

This study examines how electroencephalography (EEG) signatures are modulated during passive movements induced by electrical muscle stimulation (EMS). Specifically, we focused on three key factors: (1) motor errors, (2) stimulus-driven attention, and (3) cognitive conditions (motor imagery and waiting). We tested how error-related neural responses are modulated by the mismatch between predictions and actual sensory inputs during motor imagery. To this end, we introduced a wrist-movement paradigm combining motor imagery with EMS. Participants either imagined wrist dorsiflexion toward visual targets or passively waited. EMS was then applied to induce passive wrist movements. EMS intensity and presentation rate were manipulated so that the magnitude of stepwise motor error (physical deviation from the target) and stimulation-driven attention varied inversely, allowing us to evaluate their respective neural effects. Event-related potential (ERP) components at approximately 700 ms reflected graded evaluation of motor error. An ERP component at approximately 300 ms was modulated primarily by EMS intensity and presentation rate, consistent with stimulus-driven attention. Mu-band suppression reflected the match between predicted and actual sensory inputs. Theta-band enhancement in the absence of motor imagery suggested increased sensory unpredictability due to the lack of internally generated predictions, whereas beta-band activity may have been related to fluctuations in state prediction. To our knowledge, the proposed motor imagery with the EMS wrist-movement paradigm is the first to investigate how (1) motor errors, (2) stimulus-driven attention, and (3) cognitive conditions shape EEG signatures during passive, electrically induced movements. Understanding how these neural signals vary with mismatch between predictions and actual sensory inputs offers valuable insights into the mechanisms of error processing and sensory prediction in the human brain.