Two Brain Signal (EEG) processing applications
Leonowicz, Z (Wroclaw University of Technology, Poland)
Abstract: Trimmed estimators are a class of robust estimators of data locations which can help to improve averaging of experimental data when: number of experiments is small, data are highly nonstationary, data include outliers. Compromise between median which is very robust but discard too much information and arithmetic mean conventionally used for averaging which use all data but, due of this, is sensitive to outliers. Additional improvement of averaging can be gained by introducing advanced weighting of ordered data. Existing techniques are limited to removing only such part of raw signal which contain no or almost no components of brain origin but rather external artifacts and noise. We found a cluster of AMUSE-decorrelated components which is sensitive to AD. Room for improvement in ranking and selection of optimal (significant) components.
Keyword(s): robust estimator ; trimmed mean ; BSS ; Blind Signal Separation ; EEG ; auditory evoked potentials ; Alzheimer disease ; feature extraction ; ICA
Fulltext : http://zet10.ipee.pwr.wroc.pl/record/299/files/
Cited by: try citation search for ENY-ARTICLE-2009-238
Record created 2009-01-09, last modified 2009-01-09
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