Effect of neurokinin 1 receptor (NK1R) antagonist on mechanical paw hypersensitivity in MIA-induced osteoarthritis model

摘要

Efficient decoding of imagined upper limb movements from human electrocorticographic signals Brain computer interface (BCI) aims to help patients with motor dysfunctions by offering a surrogate motor system to restore lost functions such as the reach-and-grasp movement of the upper limbs. As such, decoding the neural signals that were recorded dur- ing upper limb movements has been a widely investigated topic. While past studies have demonstrated successful decoding of neu- ral signals obtained during real executed movements of the upper limb during various reach-and-grasp tasks, exploration on decod- ing of neural signals obtained during imagined movements has only been attempted to a limited extent. Here, we present results of continuous decoding of electrocorticographic (ECoG) signals of an epileptic patient who performed imaginations of upper limb move- ments in three-dimensional space. Features were extracted from the recorded ECoG signals by a carefully designed preprocessing pipeline that not only performed extraction, but also harnessed a selection mechanism to preemptively screen out irrelevant or noisy features. Continuous decoding strategy by multiple linear regression was then employed to train the decoder, which made predictions on the instantaneous position and velocity of the trajec- tories of imagined movement, which correlated up to a maximum correlation coefficient of 0.4 with trajectories of real executed movement. In addition to successful continuous decoding of imag- ined movements, we identified spatiotemporal neural features that were highly informative about imagined movement, and were in fact solely sufficient in generating correlation coefficients compa- rable to decoding using all relevant neural features. The findings from this study serve as important evidence of the feasibility of the prediction of imagined upper limb trajectories and suggest the pos- sibility of reducing the computational load of BCIs by exploitation of sufficiently informative neural features for upper limb movement.