Decoding hand trajectories from micro-electrocorticography in human patients.
A Kalman filter was used to decode hand trajectories from micro-electrocorticography recorded over motor cortex in human patients. In two cases, signals were recorded during stereotyped tasks, and the trajectories were decoded offline, with maximum correlation coefficients between actual and predicted trajectories of 0.51 (x-direction position) and 0.54 (y-direction position). In a third setting, a human patient with full neural control of a computer cursor acquired onscreen targets within 6.24 sec on average, with no algorithmic constraints on the output trajectory. These practical results illustrate the potential utility of signals recorded at the cortical surface with high spatial resolution, demonstrating that surface potentials contain relevant and sufficient information to drive sophisticated brain-computer interface systems.
Division of Biology, California Institute of Biology, Pasadena, CA 91125, USA. spencer.kellis@caltech.edu
Spencer
S
Kellis
Sara
S
Hanrahan
Tyler
T
Davis
Paul A
PA
House
Richard
R
Brown
Bradley
B
Greger
D000465:Algorithms; D001931:Brain Mapping / Q000379:methods; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000503:physiopathology*; D019054:Evoked Potentials, Motor; D006225:Hand / Q000503:physiopathology*; D006801:Humans; D008297:Male; D009044:Motor Cortex / Q000503:physiopathology*; D009068:Movement; D055815:Young Adult