Neural adaptation of epidural electrocorticographic (EECoG) signals during closed-loop brain computer interface (BCI) tasks.
Invasive BCI studies have classically relied on actual or imagined movements to train their neural decoding algorithms. In this study, non-human primates were required to perform a 2D BCI task using epidural microECoG recordings. The decoding weights and cortical locations of the electrodes used for control were randomly chosen and fixed for a series of daily recording sessions for five days. Over a period of one week, the subjects learned to accurately control a 2D computer cursor through neural adaptation of microECoG signals over "cortical control columns" having diameters on a the order of a few mm. These results suggest that the spatial resolution of microECoG recordings can be increased via neural plasticity.
Rouse
Adam G
AG
Department of Biomedical Engineering, Washington University, St. Louis, MO 63130, USA. arouse@wustl.edu
Moran
Daniel W
DW
D000222:Adaptation, Physiological / Q000502:physiology; D000818:Animals; D001676:Biofeedback, Psychology / Q000379:methods / Q000502:physiology*; D004388:Dura Mater / Q000503:physiopathology; D004569:Electroencephalography / Q000379:methods*; D000882:Haplorhini; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology*; D013647:Task Performance and Analysis; D014584:User-Computer Interface