## Third International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2003)## Florence, Italy |

(2) Department of Electrical Engineering, Instituto Politécnico de Leiria, Leiria, Portugal

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.