Using the electrocorticographic speech network to control a brain-computer interface in humans.

TitleUsing the electrocorticographic speech network to control a brain-computer interface in humans.
Publication TypeJournal Article
Year of Publication2011
AuthorsLeuthardt, EC, Gaona, CM, Sharma, M, Szrama, N, Roland, J, Freudenberg, Z, Solisb, J, Breshears, J, Schalk, G
JournalJ Neural Eng
Volume8
Issue3
Pagination036004
Date Published06/2011
ISSN1741-2552
Other NumbersNIHMSID: NIHMS481767
KeywordsAdult, Brain, Brain Mapping, Computer Peripherals, Electroencephalography, Evoked Potentials, Feedback, Physiological, Female, Humans, Imagination, Male, Middle Aged, Nerve Net, Speech Production Measurement, User-Computer Interface
Abstract Electrocorticography (ECoG) has emerged as a new signal platform for brain-computer interface (BCI) systems. Classically, the cortical physiology that has been commonly investigated and utilized for device control in humans has been brain signals from the sensorimotor cortex. Hence, it was unknown whether other neurophysiological substrates, such as the speech network, could be used to further improve on or complement existing motor-based control paradigms. We demonstrate here for the first time that ECoG signals associated with different overt and imagined phoneme articulation can enable invasively monitored human patients to control a one-dimensional computer cursor rapidly and accurately. This phonetic content was distinguishable within higher gamma frequency oscillations and enabled users to achieve final target accuracies between 68% and 91% within 15 min. Additionally, one of the patients achieved robust control using recordings from a microarray consisting of 1 mm spaced microwires. These findings suggest that the cortical network associated with speech could provide an additional cognitive and physiologic substrate for BCI operation and that these signals can be acquired from a cortical array that is small and minimally invasive.
URLhttp://www.ncbi.nlm.nih.gov/pubmed/21471638
DOI10.1088/1741-2560/8/3/036004
Alternate JournalJ Neural Eng
PubMed ID21471638
PubMed Central IDPMC3701859
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