A brain-computer interface using electrocorticographic signals in humans.

TitleA brain-computer interface using electrocorticographic signals in humans.
Publication TypeJournal Article
Year of Publication2004
AuthorsLeuthardt, EC, Schalk, G, Wolpaw, JR, Ojemann, JG, Moran, D
JournalJ Neural Eng
Volume1
Issue2
Pagination63-71
Date Published06/2004
ISSN1741-2560
KeywordsAdult, Brain, Communication Aids for Disabled, Computer Peripherals, Diagnosis, Computer-Assisted, Electrodes, Implanted, Electroencephalography, Evoked Potentials, Female, Humans, Imagination, Male, Movement Disorders, User-Computer Interface
Abstract Brain-computer interfaces (BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the brain can enable users to control a one-dimensional computer cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3-24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74-100% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the brain.
URLhttp://www.ncbi.nlm.nih.gov/pubmed/15876624
DOI10.1088/1741-2560/1/2/001
Alternate JournalJ Neural Eng
PubMed ID15876624
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