Toward electrocorticographic control of a dexterous upper limb prosthesis: Building brain-machine interfaces

Matthew S. Fifer, Soumyadipta Acharya, Heather L. Benz, Mohsen Mollazadeh, Nathan E. Crone, Nitish V. Thakor

Research output: Contribution to journalArticlepeer-review

52 Scopus citations


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.

Original languageEnglish (US)
Article number6153113
Pages (from-to)38-42
Number of pages5
JournalIEEE Pulse
Issue number1
StatePublished - Jan 2012

ASJC Scopus subject areas

  • Biomedical Engineering


Dive into the research topics of 'Toward electrocorticographic control of a dexterous upper limb prosthesis: Building brain-machine interfaces'. Together they form a unique fingerprint.

Cite this