LAUSR

OBJECTIVE In this manuscript, we consider factors that may affect the design of a hybrid brain-computer interface (BCI). We combine neural correlates of natural movements and interaction error-related potentials (ErrP) to perform a 3D reaching task, focusing on the impact that such factors have on the evoked ErrP signatures and in their classification. APPROACH Users attempted to control a 3D virtual interface that simulated their own hand, to reach and grasp two different objects. Three factors of interest were modulated during the experimentation: (1) execution speed of the grasping, (2) type of grasping and (3) mental strategy (motor imagery or real motion) used to produce motor commands. Thirteen healthy subjects carried out the protocol. The peaks and latencies of the ErrP were analyzed for the different factors as well as the classification performance. MAIN RESULTS ErrP are evoked for erroneous commands decoded from neural correlates of natural movements. The analysis of variance (ANOVA) analyses revealed that latency and magnitude of the most characteristic ErrP peaks were significantly influenced by the speed at which the grasping was executed, but not the type of grasp. This resulted in an greater accuracy of single-trial decoding of errors for fast movements (75.65%) compared to slow ones (68.99%). SIGNIFICANCE Understanding the effects of combining paradigms is a first step to design hybrid BCI that optimize decoding accuracy and can be deployed in motor substitution and neuro-rehabilitation applications.