Prediction of arm movement trajectories from ECoG-recordings in humans.
Electrocorticographic (ECoG) signals have been shown to contain reliable information about the direction of arm movements and can be used for on-line cursor control. These findings indicate that the ECoG is a potential basis for a brain-machine interface (BMI) for application in paralyzed patients. However, previous approaches to ECoG-BMIs were either based on classification of different movement patterns or on a voluntary modulation of spectral features. For a continuous multi-dimensional BMI control, the prediction of complete movement trajectories, as it has already been shown for spike data and local field potentials (LFPs), would be a desirable addition for the ECoG, too. Here, we examined ECoG signals from six subjects with subdurally implanted ECoG-electrodes during continuous two-dimensional arm movements between random target positions. Our results show that continuous trajectories of 2D hand position can be approximately predicted from the ECoG recorded from hand/arm motor cortex. This indicates that ECoG signals, related to body movements, can directly be transferred to equivalent controls of an external effector for continuous BMI control.
Pistohl
Tobias
T
Bernstein Center for Computational Neuroscience, Albert-Ludwigs-University, Freiburg, Germany. tobias.pistohl@biologie.uni-freiburg.de
Ball
Tonio
T
Schulze-Bonhage
Andreas
A
Aertsen
Ad
A
Mehring
Carsten
C
D000293:Adolescent; D000328:Adult; D001132:Arm / Q000503:physiopathology*; D001931:Brain Mapping; D004558:Electric Stimulation / Q000379:methods; D004569:Electroencephalography; D004827:Epilepsy / Q000503:physiopathology / Q000628:therapy; D006801:Humans; D009044:Motor Cortex / Q000503:physiopathology*; D009068:Movement / Q000502:physiology*; D009716:Numerical Analysis, Computer-Assisted; D011237:Predictive Value of Tests; D013057:Spectrum Analysis; D014584:User-Computer Interface