Third International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2003)
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.
Full Paper (reprinted with permission from Firenze University Press)
Bibliographic reference. Lima, C. S. / Silva, C. A. / Tavare, A. C. / Oliveira, J. F. (2003): "Blind source separation by independent component analysis applied to electroencephalographic signals", In MAVEBA-2003, 99-102.