Investigating sensorimotor beta burst dynamics as a robust biomarker for graded force modulation in humans

The most prominent signature associated with motor execution and motor imagery is the event-related desynchronisation and synchronisation (ERD/S) in the mu and beta bands (8-30 Hz). In the context of brain-computer interfaces (BCI), this ERD/S signature is helpful for binary decisions, such as left vs. right imagery, but it is not a robust biomarker for continuous prediction, such as precisely decoding different levels of force application. This is essential for developing better BCI applications with precise dynamic force outputs. Recent studies have revealed that sensorimotor beta bursts have a stronger relationship with motor control, even at a single-trial level, than beta band power. We, therefore, investigated whether the transient nature of beta bursts provide an alternative, but robust biomarker for BCI force decoding. Here, we designed an experiment where human participants (N = 16) performed both motor execution (ME) at four force levels (10%, 25%, 50%, and 75% of maximum voluntary contraction) and imagined exerting the same, i.e. a motor imagery (MI) task, as their electroencephalogram was recorded. We observed a clear and classical ERD pattern in the motor cortex during the ME task, whereas it was less pronounced during the MI task. After extracting sensorimotor beta bursts, we observed differences in spectral burst features between motor execution and imagery including burst amplitude, spectral width, and temporal width. Moreover, different force levels were correlated with changes in the burst amplitude and burst spectral width, specifically during motor execution. Interestingly, we found that different beta burst waveforms are associated with the different force levels and conditions. This suggests that the bursts-level features could be driven by changes in the underlying beta burst waveforms. Overall, our study shows that sensorimotor beta burst can be an important piece of the puzzle to implementing precise force control in brain-computer interface-based prosthetics.