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Toward electrocorticographic control of a dexterous upper limb prosthesis: building brain-machine interfaces
Matthew S Fifer1, Soumyadipta Acharya, Heather L Benz
1Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. msfifer@gmail.com
IEEE Pulse
|February 21, 2012
Summary
Researchers developed a brain-machine interface using electrocorticography (ECoG) signals to control a prosthetic limb. This system shows promise for advanced prosthetic control by analyzing brain activity during grasping movements.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Rehabilitation Technology
Background:
- Advancements in prosthetic limb technology, such as the Modular Prosthetic Limb (MPL), enable complex movements.
- Brain-machine interfaces (BMIs) offer a pathway to control prosthetic devices using neural signals.
- Electrocorticography (ECoG) provides a high-quality neural signal with better spatial resolution and signal-to-noise ratio compared to electroencephalography (EEG).
Purpose of the Study:
- To implement and evaluate an ECoG-based BMI system for controlling the Johns Hopkins University/Applied Physics Laboratory's Modular Prosthetic Limb (MPL).
- To identify reliable neural features from ECoG signals that correlate with grasping movements for closed-loop prosthetic control.
Main Methods:
- An ECoG-based system was implemented in an epilepsy monitoring unit.
- Patients with implanted ECoG grids performed recorded finger and grasp movements.
- Analysis focused on low-frequency local motor potentials (LMPs) and high gamma band (70-150 Hz) ECoG power.
Main Results:
- Low-frequency local motor potentials (LMPs) showed a strong correlation with grasping parameters.
- High gamma frequency (70-150 Hz) ECoG power also correlated well with grasping parameters.
- These identified features are suitable candidates for closed-loop control of the MPL.
Conclusions:
- ECoG signals, specifically LMPs and high gamma power, are effective for controlling prosthetic limbs.
- This ECoG-based BMI system demonstrates potential for intuitive and precise prosthetic limb control.
- The findings support the use of ECoG for developing advanced brain-machine interfaces for prosthetics.
