Parallel Factor Analysis as an exploratory tool for wavelet transformed event-related EEG

• Published in: NeuroImage (Orlando, Fla.) Vol. 29; no. 3; pp. 938 - 947
• Main Authors: Mørup, Morten, Hansen, Lars Kai, Herrmann, Christoph S., Parnas, Josef, Arnfred, Sidse M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.02.2006; Elsevier Limited
• Subjects: Adult; Algorithms; Arrays; Brain research; CANDECOMP; Channel–time–frequency analysis; Data Interpretation, Statistical; Decomposition; Electroencephalography - statistics & numerical data; ERP; Evoked Potentials - physiology; Evoked Potentials, Visual - physiology; Factor Analysis, Statistical; Gamma coherence; Humans; ITPC; Male; Multi-way arrays; Occipital Lobe - physiology; PARAFAC; Photic Stimulation; Principal components analysis; Studies; Wavelet analysis; CANDECOMP; ERP; Channel–time–frequency analysis; PARAFAC; Wavelet analysis; Decomposition; ITPC; Gamma coherence; Multi-way arrays

In the decomposition of multi-channel EEG signals, principal component analysis (PCA) and independent component analysis (ICA) have widely been used. However, as both methods are based on handling two-way data, i.e. two-dimensional matrices, multi-way methods might improve the interpretation of freq...