Toward Electrocorticographic Control of a Dexterous Upper Limb Prosthesis: Building Brain-Machine Interfaces
Year: 2012
Type: article
Abstract: 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 (70,150 Hz) range correlate well with grasping parameters, and they stand out as good candidate features for closed-loop control of the MPL.
Source: IEEE Pulse
Authors: M. S. Fifer, S. Acharya, H. L. Benz, M. Mollazadeh, N. E. Crone, N. V. Thakor
Institutions: Johns Hopkins University, Johns Hopkins Medicine
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2013619029