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Enabling computer decisions based on EEG inputMultilayer neural networks were successfully trained to classify segments of 12-channel electroencephalogram (EEG) data into one of five classes corresponding to five cognitive tasks performed by a subject. Independent component analysis (ICA) was used to segregate obvious artifact EEG components from other sources, and a frequency-band representation was used to represent the sources computed by ICA. Examples of results include an 85% accuracy rate on differentiation between two tasks, using a segment of EEG only 0.05 s long and a 95% accuracy rate using a 0.5-s-long segment.
Document ID
20050154891
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Culpepper, Benjamin J.
(NASA Ames Research Center Moffett Field, CA United States)
Keller, Robert M.
Date Acquired
August 23, 2013
Publication Date
December 1, 2003
Publication Information
Publication: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Volume: 11
Issue: 4
ISSN: 1534-4320
Subject Category
Man/System Technology And Life Support
Distribution Limits
Public
Copyright
Other
Keywords
Validation Studies
Evaluation Studies

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