Heart rate variability predicts decline in sensorimotor rhythm control

Objective. Voluntary control of sensorimotor rhythms (SMRs, 8–12 Hz) can be used for brain–computer interface (BCI)-based operation of an assistive hand exoskeleton, e.g. in finger paralysis after stroke. To gain SMR control, stroke survivors are usually instructed to engage in motor imagery (MI) or to attempt moving the paralyzed fingers resulting in task- or event-related desynchronization (ERD) of SMR (SMR-ERD). However, as these tasks are cognitively demanding, especially for stroke survivors suffering from cognitive impairments, BCI control performance can deteriorate considerably over time. Therefore, it would be important to identify biomarkers that predict decline in BCI control performance within an ongoing session in order to optimize the man–machine interaction scheme. Approach. Here we determine the link between BCI control performance over time and heart rate variability (HRV). Specifically, we investigated whether HRV can be used as a biomarker to predict decline of SMR-ERD control across 17 healthy participants using Granger causality. SMR-ERD was visually displayed on a screen. Participants were instructed to engage in MI-based SMR-ERD control over two consecutive runs of 8.5 min each. During the 2nd run, task difficulty was gradually increased. Main results. While control performance ( p = .18) and HRV ( p = .16) remained unchanged across participants during the 1st run, during the 2nd run, both measures declined over time at high correlation (performance: −0.61%/10 s, p = 0; HRV: −0.007 ms/10 s, p < .001). We found that HRV exhibited predictive characteristics with regard to within-session BCI control performance on an individual participant level ( p < .001). Significance. These results suggest that HRV can predict decline in BCI performance paving the way for adaptive BCI control paradigms, e.g. to individualize and optimize assistive BCI systems in stroke.