Format: HTML | BibTeX | DC | EndNote | NLM | MARC | MARCXML
000000299 001__ 299
000000299 037__ $$aENY-ARTICLE-2009-238
000000299 041__ $$aeng
000000299 100__ $$aLeonowicz, Z$$uWroclaw University of Technology, Poland
000000299 245__ $$aTwo Brain Signal (EEG) processing applications
000000299 260__ $$c2007-03-29
000000299 300__ $$a37p
000000299 520__ $$aTrimmed 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.
000000299 6531_ $$arobust estimator
000000299 6531_ $$atrimmed mean
000000299 6531_ $$aBSS
000000299 6531_ $$aBlind Signal Separation
000000299 6531_ $$aEEG
000000299 6531_ $$aauditory evoked potentials
000000299 6531_ $$aAlzheimer disease
000000299 6531_ $$afeature extraction
000000299 6531_ $$aICA
000000299 8560_ $$fleon99@pwr.wroc.pl
000000299 8564_ $$uhttp://zet10.ipee.pwr.wroc.pl/record/299/files/$$zAccess to Fulltext
000000299 909CO $$ooai:zet10.pwr.wroc.pl:299$$pglobal
000000299 980__ $$aARTICLE