Evaluation of PCA and ICA of simulated ERPs: Promax vs. infomax rotations

Independent components analysis (ICA) and principal components analysis (PCA) are methods used to analyze event‐related potential (ERP) and functional imaging (fMRI) data. In the present study, ICA and PCA were directly compared by applying them to simulated ERP datasets. Specifically, PCA was used to generate a subspace of the dataset followed by the application of PCA Promax or ICA Infomax rotations. The simulated datasets were composed of real background EEG activity plus two ERP simulated components. The results suggest that Promax is most effective for temporal analysis, whereas Infomax is most effective for spatial analysis. Failed analyses were examined and used to devise potential diagnostic strategies for both rotations. Finally, the results also showed that decomposition of subject averages yield better results than of grand averages across subjects. Hum Brain Mapp 2006. © 2006 Wiley‐Liss, Inc.

[1]  Silke Dodel,et al.  Localization of brain activity - blind separation for fMRI data , 2000, Neurocomputing.

[2]  W. Velicer,et al.  The Effects of Overextraction on Factor and Component Analysis. , 1992, Multivariate behavioral research.

[3]  R. Cattell.  The Scree Test For The Number Of Factors. , 1966, Multivariate behavioral research.

[4]  P. Berg,et al.  Optimizing principal components analysis of event-related potentials: Matrix type, factor loading weighting, extraction, and rotations , 2005, Clinical Neurophysiology.

[5]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[6]  Terrence J. Sejnowski,et al.  Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources , 1999, Neural Computation.

[7]  Joseph Dien,et al.  Issues in the application of the average reference: Review, critiques, and recommendations , 1998 .

[8]  Sakiko Yoshikawa,et al.  Emotional expression boosts early visual processing of the face: ERP recording and its decomposition by independent component analysis , 2001, Neuroreport.

[9]  André Beauducel,et al.  Misallocation of variance in event-related potentials: simulation studies on the effects of test power, topography, and baseline-to-peak versus principal component quantifications , 2003, Journal of Neuroscience Methods.

[10]  C E Tenke,et al.  Dissociation of brain ERP topographies for tonal and phonetic oddball tasks. , 1998, Psychophysiology.

[11]  Ricardo Vig.  Overlearning in Marginal Distribution-Based ICA: Analysis and Solutions , 2003 .

[12]  R N Vigário,et al.  Extraction of ocular artefacts from EEG using independent component analysis. , 1997, Electroencephalography and clinical neurophysiology.

[13]  Terrence J. Sejnowski,et al.  Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Sub-Gaussian and Super-Gaussian Sources , 1999, Neural Comput..

[14]  中田 力.  Integrated human brain science : theory, method, application (music) , 2000 .

[15]  Richard L. Gorsuch,et al.  Effects of under- and overextraction on principal axis factor analysis with varimax rotation. , 1996 .

[16]  H. Kaiser.  The varimax criterion for analytic rotation in factor analysis , 1958 .

[17]  R. Cattell,et al.  A general plasmode (No. 30-10-5-2) for factor analytic exercises and research. , 1967 .

[18]  Daniel S. Ruchkin,et al.  The Late Positive Complex , 1984, Annals of the New York Academy of Sciences.

[19]  J. Edward Jackson,et al.  A User's Guide to Principal Components. , 1991 .

[20]  Edward E. Cureton,et al.  The weighted varimax rotation and the promax rotation , 1975 .

[21]  R. Verleger,et al.  Multivariate methods in biosignal analysis : application of principal component analysis to event-related potentials , 1991 .

[22]  H. Schimmel.  The (�) Reference: Accuracy of Estimated Mean Components in Average Response Studies , 1967, Science.

[23]  Hae-Jeong Park,et al.  Independent component model for cognitive functions of multiple subjects using [15O]H2O PET images , 2003, Human brain mapping.

[24]  T. Sejnowski,et al.  Analysis and visualization of single‐trial event‐related potentials , 2001, Human brain mapping.

[25]  T. Picton,et al.  The N1 wave of the human electric and magnetic response to sound: a review and an analysis of the component structure. , 1987, Psychophysiology.

[26]  H. Jasper.  Report of the committee on methods of clinical examination in electroencephalography , 1958 .

[27]  D. Regan.  Human brain electrophysiology: Evoked potentials and evoked magnetic fields in science and medicine , 1989 .

[28]  Terrence J. Sejnowski,et al.  Independent Component Analysis of Simulated ERP Data , 2000 .

[29]  C. Tenke,et al.  Optimizing PCA methodology for ERP component identification and measurement: theoretical rationale and empirical evaluation , 2003, Clinical Neurophysiology.

[30]  T. Sejnowski,et al.  Functionally Independent Components of the Late Positive Event-Related Potential during Visual Spatial Attention , 1999, The Journal of Neuroscience.

[31]  E. Donchin,et al.  Spatiotemporal analysis of the late ERP responses to deviant stimuli. , 2001, Psychophysiology.

[32]  Andrew L. Comrey,et al.  Common methodological problems in factor analytic studies. , 1978 .

[33]  J. Rohrbaugh,et al.  Cortical slow negative waves following non-paired stimuli: effects of task factors. , 1978, Electroencephalography and clinical neurophysiology.

[34]  D H Fender,et al.  The dimensionality of the human visual evoked scalp potential. , 1976, Electroencephalography and clinical neurophysiology.

[35]  C. C. Wood,et al.  Event-related potentials, lexical decision and semantic priming. , 1985, Electroencephalography and clinical neurophysiology.

[36]  Rolf Weitkunat,et al.  Digital Biosignal Processing , 1991 .

[37]  Werner Lutzenberger,et al.  Principal component analysis of slow brain potentials during six second anticipation intervals , 1981, Biological Psychology.

[38]  M. Greicius,et al.  Default-Mode Activity during a Passive Sensory Task: Uncoupled from Deactivation but Impacting Activation , 2004, Journal of Cognitive Neuroscience.

[39]  D. Ruchkin,et al.  Multiple sources of P3b associated with different types of information. , 1990, Psychophysiology.

[40]  S Makeig,et al.  Functionally independent components of early event-related potentials in a visual spatial attention task. , 1999, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[41]  D. Tucker,et al.  Parametric analysis of event-related potentials in semantic comprehension: evidence for parallel brain mechanisms. , 2003, Brain research. Cognitive brain research.

[42]  H. Harman.  Modern factor analysis , 1961 .

[43]  Mark H. Johnson,et al.  Recording and Analyzing High-Density Event-Related Potentials With Infants Using the Geodesic Sensor Net , 2001, Developmental neuropsychology.

[44]  Joseph Dien,et al.  Addressing Misallocation of Variance in Principal Components Analysis of Event-Related Potentials , 2004, Brain Topography.

[45]  C. M. Yee,et al.  Affective valence and information processing. , 1987, Electroencephalography and clinical neurophysiology. Supplement.

[46]  D. Tucker,et al.  Localization of Auditory Evoked Potentials Related to Selective Intermodal Attention , 1997, Journal of Cognitive Neuroscience.

[47]  C C Wood,et al.  Principal component analysis of event-related potentials: simulation studies demonstrate misallocation of variance across components. , 1984, Electroencephalography and clinical neurophysiology.

[48]  John E Richards,et al.  Recovering dipole sources from scalp-recorded event-related-potentials using component analysis: principal component analysis and independent component analysis. , 2004, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[49]  Atsushi Matsumoto,et al.  Linking semantic priming effect in functional MRI and event-related potentials , 2005, NeuroImage.

[50]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[51]  Tzyy-Ping Jung,et al.  Independent Component Analysis of Electroencephalographic Data , 1995, NIPS.

[52]  Miller Ga,et al.  Affective valence and information processing. , 1987 .

[53]  Robert M. Chapman,et al.  Brain responses related to semantic meaning , 1978, Brain and Language.

[54]  Lars Kai Hansen,et al.  Blind detection of independent dynamic components , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[55]  J. E. Jackson.  A User's Guide to Principal Components , 1991 .

[56]  T. Sejnowski,et al.  Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects , 2000, Clinical Neurophysiology.

[57]  D. Ruchkin,et al.  The late positive complex. Advances and new problems. , 1984, Annals of the New York Academy of Sciences.

[58]  E. Donchin.  Multivariate analysis of event-related potential data: A tutorial review , 1978 .

[59]  Ricardo Vigário,et al.  Overlearning in Marginal Distribution-Based ICA: Analysis and Solutions , 2003, J. Mach. Learn. Res..

[60]  P. O. White,et al.  PROMAX: A QUICK METHOD FOR ROTATION TO OBLIQUE SIMPLE STRUCTURE , 1964 .

[61]  D Friedman,et al.  Multiple late positive potentials in two visual discrimination tasks. , 1981, Psychophysiology.

[62]  E. Donchin,et al.  Localization of the event-related potential novelty response as defined by principal components analysis. , 2003, Brain research. Cognitive brain research.

[63]  John H. Robinson,et al.  P300 and Response Selection: A New Look Using Independent-Components Analysis , 2004, Brain Topography.

[64]  J. Rohrbaugh,et al.  Current trends in event-related potential research , 1987 .

[65]  John Polich,et al.  Semantic categorization and event-related potentials , 1985, Brain and Language.

[66]  Todd C. Handy,et al.  Event-related potentials : a methods handbook , 2005 .

[67]  E. Donchin,et al.  A componential analysis of the ERP elicited by novel events using a dense electrode array. , 1999, Psychophysiology.

[68]  R. Cooper,et al.  4 The Principal Components of Auditory Target Detection , 1983 .

[69]  S Makeig,et al.  Blind separation of auditory event-related brain responses into independent components. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[70]  E Donchin,et al.  Brain potentials as indices of orthographic and phonological interaction during word matching. , 1987, Journal of experimental psychology. Learning, memory, and cognition.

[71]  J. Edward Jackson,et al.  A User's Guide to Principal Components: Jackson/User's Guide to Principal Components , 2004 .

[72]  Joseph Dien,et al.  Differential lateralization of trait anxiety and trait fearfulness: Evoked potential correlates , 1998 .

[73]  R. Darlington,et al.  Factor Analysis , 2008 .

[74]  J. E. Jackson,et al.  Factor analysis, an applied approach , 1983 .

[75]  O Bertrand,et al.  A theoretical justification of the average reference in topographic evoked potential studies. , 1985, Electroencephalography and clinical neurophysiology.

[76]  N. Squires,et al.  Two varieties of long-latency positive waves evoked by unpredictable auditory stimuli in man. , 1975, Electroencephalography and clinical neurophysiology.

[77]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[78]  V D Calhoun,et al.  Spatial and temporal independent component analysis of functional MRI data containing a pair of task‐related waveforms , 2001, Human brain mapping.