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Analysis of EEG data using a geometric-based ICA algorithm

Bauer, Ch., Alvarez, M. R., Lang, Elmar W. and Puntonet, C. G. (2000) Analysis of EEG data using a geometric-based ICA algorithm. Verhandlungen der Deutschen Physikalischen Gesellschaft. Reihe 6 35, p. 485.

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Electroencephalographic (EEG) signals are used as a non-invasive clinical tool for the diagnosis and treatment of brain deaseases. However, they are often disturbed by artifacts which limit the possibility to interpret the data. Independent Component Analysis (ICA) is able to recover n independent sources [s\vec](t) which are linearly mixed by an unknown mixing process [x\vec](t) = A [s\vec](t). ...


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Item type:Article
Additional Information (public):Refereed Extended Abstract
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Dewey Decimal Classification:500 Science > 570 Life sciences
Refereed:Yes, this version has been refereed
Created at the University of Regensburg:Unknown
Item ID:16716
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