Referenced Items are not available for this document.
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.
Published in:
Neural Systems and Rehabilitation Engineering, IEEE Transactions on
(Volume:11
,
Issue:
4
)
Date of Publication: Dec. 2003
- Page(s):
- 354 - 360
- ISSN :
- 1534-4320
- INSPEC Accession Number:
- 7966260
- Digital Object Identifier :
- 10.1109/TNSRE.2003.819788
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 30 January 2004
- Issue Date :
- Dec. 2003
- Sponsored by :
- IEEE Engineering in Medicine and Biology Society
- PubMed ID :
- 14960110
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