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OBJECTIVE: This paper presents a hybrid BCI combining neural correlates of natural movements and interaction error-related potentials (ErrP) to perform a 3D reaching task. It focuses on the impact that design factors of such a hybrid BCI have on the 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) motor commands generated by motor imagery or real motion. 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 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. Invariance of ErrP to different type of grasping movements and mental strategies proves this type of hybrid interface to be useful for the design of out of the lab applications such as the operation/control of prosthesis. Factors such as the speed of the movements have to be carefully tuned in order to optimize the performance of the system.
Fields of Expertise
- Human- & Biotechnology
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