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

@article{citeulike:6067479, abstract = {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.}, author = {M{\o}rup, Morten and Hansen, Lars K. and Herrmann, Christoph S. and Parnas, Josef and Arnfred, Sidse M.}, citeulike-article-id = {6067479}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.neuroimage.2005.08.005}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S1053811905005896}, day = {01}, doi = {10.1016/j.neuroimage.2005.08.005}, issn = {10538119}, journal = {NeuroImage}, keywords = {eeg, independent\_component\_analysis, principal\_component\_analysis, time\_frequency, wavelets}, month = {February}, number = {3}, pages = {938--947}, posted-at = {2009-11-04 12:13:26}, priority = {2}, title = {Parallel Factor Analysis as an exploratory tool for wavelet transformed event-related EEG}, url = {http://dx.doi.org/10.1016/j.neuroimage.2005.08.005}, volume = {29}, year = {2006} }

TY - JOUR ID - citeulike:6067479 L3 - citeulike-article-id:6067479 N2 - 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. IS - 3 JF - NeuroImage SN - 10538119 EP - 947 TI - Parallel Factor Analysis as an exploratory tool for wavelet transformed event-related EEG VL - 29 SP - 938 KW - eeg KW - independent_component_analysis KW - principal_component_analysis KW - time_frequency KW - wavelets AU - Mørup, Morten AU - Hansen, Lars AU - Herrmann, Christoph AU - Parnas, Josef AU - Arnfred, Sidse PY - 2006/02/01/ UR - http://dx.doi.org/10.1016/j.neuroimage.2005.08.005 ER -