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Analysis of EEG data using a geometric-based ICA algorithm

Bauer, Ch. ; Alvarez, M. R. ; Lang, Elmar W. ; Puntonet, C. G.


Abstract

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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