Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition

Independent Component Analysis (ICA) has proven to be an effective data driven method for analyzing EEG data, separating signals from temporally and functionally independent brain and non-brain source processes and thereby increasing their definition. Dimension reduction by Principal Component Analy...

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Vydané v:	NeuroImage (Orlando, Fla.) Ročník 175; s. 176 - 187
Hlavní autori:	Artoni, Fiorenzo, Delorme, Arnaud, Makeig, Scott
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Inc 15.07.2018 Elsevier Limited Elsevier
Predmet:	Adult Brain - physiology Brain research Brain Waves - physiology Cognitive science Data Interpretation, Statistical Data processing Decomposition Dipolarity EEG Electroencephalogram, EEG Electroencephalography Electroencephalography - methods Electroencephalography - standards Electromyography Female Humans Independent component analysis, ICA Localization Male Neuroscience Noise Pattern Recognition, Visual - physiology Principal Component Analysis Principal component analysis, PCA Principal components analysis Reliability Signal Processing, Computer-Assisted Source localization Young Adult Independent component analysis, ICA Principal component analysis, PCA Reliability Source localization Electroencephalogram, EEG Dipolarity
ISSN:	1053-8119, 1095-9572, 1095-9572
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