Document Type
Article
Department
Computer Science (HMC)
Publication Date
12-2003
Abstract
Multilayer 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.
Rights Information
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Terms of Use & License Information
DOI
10.1109/TNSRE.2003.819788
Recommended Citation
Culpepper, Benjamin J., and Robert M. Keller. "Enabling Computer Decisions Based on EEG Input." IEEE Transactions on Neural Systems and Rehabilitation Engineering 11.4 (December 2003): 354-360. DOI: 10.1109/TNSRE.2003.819788
Comments
Previously linked to as: http://ccdl.libraries.claremont.edu/u?/irw,236.
Publisher pdf, posted with permission.
The article may be found at http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=28199&arnumber=1261745&count=19&index=4