Objective: Brain-computer interfaces (BCIs) are being developed to restore reach and grasping movements of paralyzed individuals. Recent studies have shown that the kinetics of grasping movement, such as grasp force, can be successfully decoded from electrocorticography (ECoG) signals, and that the high-frequency band (HFB) power changes provide discriminative information that contribute to an accurate decoding of grasp force profiles. However, as the models used in these studies contained simultaneous information from multiple spectral features over multiple areas in the brain, it remains unclear what parameters of movement and force are encoded by the HFB signals and how these are represented temporally and spatially in the SMC.
Approach: To investigate this, and to gain insight in the temporal dynamics of the HFB during grasping, we continuously modelled the ECoG HFB response recorded from nine individuals with epilepsy temporarily implanted with ECoG grids, who performed three different grasp force tasks.
Main results: We show that a model based on the force onset and offset consistently provides a better fit to the HFB power responses when compared with a model based on the force magnitude, irrespective of electrode location.
Significance: Our results suggest that HFB power, although potentially useful for continuous decoding, is more closely related to the changes in movement. This finding may potentially contribute to the more natural decoding of grasping movement in neural prosthetics.