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

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...

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Published in:NeuroImage (Orlando, Fla.) Fla.), 2006-02, Vol.29 (3), p.938-947
Main Authors: Mørup, Morten, Hansen, Lars Kai, Herrmann, Christoph S., Parnas, Josef, Arnfred, Sidse M.
Format: Article
Language:English
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
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Summary: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 frequency transformed multi-channel EEG of channel × frequency × time data. The multi-way decomposition method Parallel Factor (PARAFAC), also named Canonical Decomposition (CANDECOMP), was recently used to decompose the wavelet transformed ongoing EEG of channel × frequency × time (Miwakeichi, F., Martinez-Montes, E., Valdes-Sosa, P.A., Nishiyama, N., Mizuhara, H., Yamaguchi, Y., 2004. Decomposing EEG data into space–time–frequency components using parallel factor analysis. Neuroimage 22, 1035–1045). In this article, PARAFAC is used for the first time to decompose wavelet transformed event-related EEG given by the inter-trial phase coherence (ITPC) encompassing ANOVA analysis of differences between conditions and 5-way analysis of channel × frequency × time × subject × condition. A flow chart is presented on how to perform data exploration using the PARAFAC decomposition on multi-way arrays. This includes (A) channel × frequency × time 3-way arrays of F test values from a repeated measures analysis of variance (ANOVA) between two stimulus conditions; (B) subject-specific 3-way analyses; and (C) an overall 5-way analysis of channel × frequency × time × subject × condition. The PARAFAC decompositions were able to extract the expected features of a previously reported ERP paradigm: namely, a quantitative difference of coherent occipital gamma activity between conditions of a visual paradigm. Furthermore, the method revealed a qualitative difference which has not previously been reported. The PARAFAC decomposition of the 3-way array of ANOVA F test values clearly showed the difference of regions of interest across modalities, while the 5-way analysis enabled visualization of both quantitative and qualitative differences. Consequently, PARAFAC is a promising data exploratory tool in the analysis of the wavelets transformed event-related EEG.
ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2005.08.005