Document details

Blind source separation by independent component analysis applied to electroenc...

Author(s): Lima, C. S. cv logo 1 ; Silva, Carlos A. cv logo 2 ; Tavares, Adriano cv logo 3 ; Oliveira, Jorge F. cv logo 4

Date: 2003

Persistent ID: http://hdl.handle.net/1822/2048

Origin: RepositóriUM - Universidade do Minho

Subject(s): Blind source separation; EEG signals; ICA


Description
Independent Component Analysis (ICA) is a statistical based method, which goal is to find a linear transformation to apply to an observed multidimensional random vector such that its components become as statistically independent from each other as possible. Usually the Electroencephalographic (EEG) signal is hard to interpret and analyse since it is corrupted by some artifacts which originates the rejection of contaminated segments and perhaps in an unacceptable loss of data. The ICA filters trained on data collected during EEG sessions can identify statistically independent source channels which could then be further processed by using event-related potential (ERP), event-related spectral perturbation (ERSP) or other signal processing techniques. This paper describes, as a preliminary work, the application of ICA to EEG recordings of the human brain activity, showing its applicability.
Document Type Conference Object
Language English
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