TY - JOUR
T1 - Toward electrocorticographic control of a dexterous upper limb prosthesis
T2 - Building brain-machine interfaces
AU - Fifer, Matthew S.
AU - Acharya, Soumyadipta
AU - Benz, Heather L.
AU - Mollazadeh, Mohsen
AU - Crone, Nathan E.
AU - Thakor, Nitish V.
N1 - Funding Information:
This work was launched by the DARPA Revolutionary Prosthetics (RP2009) program Phase I and II that funded the development of the JHU/APL prosthetic limb and decoding work. Recent work on human subjects was funded by the Phase III DARPA funding to JHU/APL and clinical investigation by the National Institute of Neurological Disorder and Stroke Grant 3R01NS040596-09S1.
PY - 2012/1
Y1 - 2012/1
N2 - One of the most exciting and compelling areas of research and development is building brain machine interfaces (BMIs) for controlling prosthetic limbs. Prosthetic limb technology is advancing rapidly, and the modular prosthetic limb (MPL) of the Johns Hopkins University/ Applied Physics Laboratory (JHU/APL) permits actuation with 17 degrees of freedom in 26 articulating joints. There are many signals from the brain that can be leveraged, including the spiking rates of neurons in the cortex, electrocorticographic (ECoG) signals from the surface of the cortex, and electroencephalographic (EEG) signals from the scalp. Unlike microelectrodes that record spikes, ECoG does not penetrate the cortex and has a higher spatial specificity, signal-to-noise ratio, and bandwidth than EEG signals. We have implemented an ECoG-based system for controlling the MPL in the Johns Hopkins Hospital Epilepsy Monitoring Unit, where patients are implanted with ECoG electrode grids for clinical seizure mapping and asked to perform various recorded finger or grasp movements. We have shown that low-frequency local motor potentials (LMPs) and ECoG power in the high gamma frequency (70150 Hz) range correlate well with grasping parameters, and they stand out as good candidate features for closed-loop control of the MPL.
AB - One of the most exciting and compelling areas of research and development is building brain machine interfaces (BMIs) for controlling prosthetic limbs. Prosthetic limb technology is advancing rapidly, and the modular prosthetic limb (MPL) of the Johns Hopkins University/ Applied Physics Laboratory (JHU/APL) permits actuation with 17 degrees of freedom in 26 articulating joints. There are many signals from the brain that can be leveraged, including the spiking rates of neurons in the cortex, electrocorticographic (ECoG) signals from the surface of the cortex, and electroencephalographic (EEG) signals from the scalp. Unlike microelectrodes that record spikes, ECoG does not penetrate the cortex and has a higher spatial specificity, signal-to-noise ratio, and bandwidth than EEG signals. We have implemented an ECoG-based system for controlling the MPL in the Johns Hopkins Hospital Epilepsy Monitoring Unit, where patients are implanted with ECoG electrode grids for clinical seizure mapping and asked to perform various recorded finger or grasp movements. We have shown that low-frequency local motor potentials (LMPs) and ECoG power in the high gamma frequency (70150 Hz) range correlate well with grasping parameters, and they stand out as good candidate features for closed-loop control of the MPL.
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U2 - 10.1109/MPUL.2011.2175636
DO - 10.1109/MPUL.2011.2175636
M3 - Article
C2 - 22344950
AN - SCOPUS:84857474253
SN - 2154-2287
VL - 3
SP - 38
EP - 42
JO - IEEE Pulse
JF - IEEE Pulse
IS - 1
M1 - 6153113
ER -