Bauer, Ch. and Alvarez, M. R. and 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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|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|
|Deposited on:||23 Sep 2010 07:13|
|Last modified:||23 Sep 2010 07:13|