EP Component Identification and Measurement by Principal Components-Analysis

Chapman, Robert M.; McCrary, John W.; Chapman, R.M.; Mccrary, J.W.

doi:10.1006/brcg.1995.1262

Cited by 44 publications

(60 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mean correct responses for the three groups of children (aboveaverage, average, and below-average) for the three stimulus classes of words, orthographically incorrect but phonologically correct ("sounds like") words, and non-ERPs and children and, subsequently, in determining if the variability characterized by the different PCA extracted factors resulted from systematic changes in the independent variables under investigation (Rockstroh, Elbert, Birbaumer, & Lutzenberger, 1982, p. 63). When questions were raised regarding the misallocation of variance in a PCA analysis across immediately adjacent peaks, Wood and McCarthy (1984) noted that traditional amplitude and latency approaches were "no less subject to the problem of component overlap" (p. 258; see also Chapman & McCrary, 1995). Furthermore, when sufficient power is available, the likelihood of misallocation is marginalized.…”

Section: Electrophysiological Resultsmentioning

confidence: 99%

Below-Average, Average, and Above-Average Readers Engage Different and Similar Brain Regions While Reading

et al. 2006

View full text Add to dashboard Cite

Event-related potentials (ERPs) were recorded from 27 children (14 girls, 13 boys) who varied in their reading skill levels. Both behavior performance measures recorded during the ERP word classification task and the ERP responses themselves discriminated between children with above-average, average, and below-average reading skills. ERP amplitudes and peak latencies decreased as reading skills increased. Furthermore, hemisphere differences increased with higher reading skill levels. Sex differences were also related to ERP amplitude variations across the scalp. However, ERPs recorded from boys and girls did not differ as a function of differences in the children's reading levels.

show abstract

Section: Electrophysiological Resultsmentioning

confidence: 99%

Below-Average, Average, and Above-Average Readers Engage Different and Similar Brain Regions While Reading

et al. 2006

View full text Add to dashboard Cite

show abstract

“…Because the variance of the full data pattern defines the components, PCA allows cognitively important components to emerge in the context of the overall variance pattern [Chapman and McCrary, 1995;van Boxtel, 1998]. …”

Section: Introductionmentioning

confidence: 99%

The time course of brain activity in reading English and Chinese: An ERP study of Chinese bilinguals

Liu

Perfetti

2003

Human Brain Mapping

158

105

View full text Add to dashboard Cite

Abstract:Chinese bilinguals performed a delayed naming task, reading both Chinese characters and English words, while EEGs were recorded by a 128-channel system. Principle component analysis (PCA) of Event Related Potentials (ERP) from the onset of the stimulus suggested a temporal unfolding of graphic, phonological, and semantic processing that depended on both language and word frequency. At 150 msec, Chinese produced an earlier and higher amplitude shift (N150) than English. At 250 msec, frequency effects were significant for both Chinese and English, but at 450 msec, only the English frequency effect was reliable. Source localization analysis by Low Resolution Electromagnetic Tomography (LORETA) showed bilateral occipital (left BA 17, right BA 18) visual processing of Chinese characters with left occipital only (left BA 17) for English high-frequency words. Low-frequency English words showed activation bilaterally, but with a more diffused and extended temporal pattern. Right prefrontal area (BA 10) was found to be strongly activated in the mid latency (300 -400 msec) period of Chinese character naming, whereas English word naming showed more medial frontal (BA 8, and 10) activation. A post 450-msec visual verification was found to be general for both writing systems. Hum. Brain Mapping 18:167-175, 2003.

show abstract

“…The use of PCA as a tool for the study ofERPs was advocated by Donchin (1966). Tutorial reviews of the use of PCA for ERP data are given by Chapman and McCrary (1995), Donchin and Heffley (1978), and Glaser and Ruchkin (1976), among others. The way in which PCA is most frequently used is to arrange the recorded ERPs so that the successive time points of the single ERPs are treated as variables in the PCA program (often a statistical package such as SAS, SPSS, or BMDP).…”

Section: Instruction At S2 Slowmentioning

confidence: 99%

“…However, there is no a priori reason why the standard deviations ofall variables are the same, even when identical measurement units are used. The difference between the PCA ofthe covariance matrix and that of the correlation matrix is usually insignificant for typical ERP data (Chapman & McCrary, 1995). Practical considerations may guide the choice between the covariance and the correlation matrix, such as the analysis package available.…”

Section: Association Matrixmentioning

confidence: 99%

Computational and statistical methods for analyzing event-related potential data

Boxtel

1998

Behavior Research Methods, Instruments, & Computers

View full text Add to dashboard Cite

Some computational and statistical techniques that can be used in the analysis of event-related potential (ERP) data are demonstrated. The techniques are fairly elementary but go one step further than do simple area measurement or peak picking, which are most often used in ERP analysis. Both amplitude and latency measurement techniques are considered. Principal components analysis (peA) and methods for electromyographic onset determination are presented in detail, and Woodyfiltering is discussed briefly. The techniques are introduced in a nontechnical, tutorial review style. One and the same existing data set is presented, to which the techniques are applied, and practical guidelines for their use are given. The methods are demonstrated against a background of theoretical notions that are related to the definition of ERP components.The electroencephalogram (EEG) is the record of the potential differences between electrodes placed on the (human) scalp. EEG records can be classified into two categories: the spontaneous or background EEG and event-related potentials (ERPs). The background EEG consists of periodic voltage fluctuations, which can be classified according to their frequency content-for instance, alpha (8-12 Hz), beta (13-30 Hz), gamma (31-50 Hz), delta (0.5-4 Hz), and theta (5-7 Hz). ERPs are discrete waveforms that are associated with an event. They are hidden within the background EEG and are visualized by averaging discrete EEG epochs that are synchronized to meaningful task events, such as the presentation ofa stimulus or the occurrence ofa response. Since the background EEG is not assumed to have a temporal relationship to the synchronization event, averaging of discrete EEG epochs will, when a sufficiently large number of epochs (trials) are averaged, result in its attenuation. The activity that does have a temporal relation to the event-the ERP-will then be enhanced in relation to the background EEG and thus become visible. If plotted in time, ERPs appear as a series of positive and negative deflections, which are thought to be the manifestation of underlying ERP components.This paper was prepared while the author was supported by NWO Grant 575-270-082B, awarded to M. W van der Molen and C. H. M. Brunia. The section on principal components analysis benefited from discussions with Marcel Croon of the Department of Statistics, Tilburg University. The useful suggestions ofMartin Eimer, Emanuel Donchin, and two anonymous reviewers are gratefully acknowledged. Correspondence concerning this paper should be addressed to G. 1. M. van Boxtel, Department of Psychology (P607), Tilburg University, P.O. Box 90153, 5000 LE Tilburg, The Netherlands (e-mail: g.j.m.vboxtel@kub.nl). 87ERP components may be exogenous or endogenousthat is, evoked by events that are extrinsic or intrinsic to the nervous system. The exogenous ERP components occur near the eliciting event, and the time relation to that event is very stable. When a stimulus is presented, for instance, the exogenous ERP components have an early onset l...

show abstract

EP Component Identification and Measurement by Principal Components-Analysis

Cited by 44 publications

References 0 publications

Below-Average, Average, and Above-Average Readers Engage Different and Similar Brain Regions While Reading

Below-Average, Average, and Above-Average Readers Engage Different and Similar Brain Regions While Reading

The time course of brain activity in reading English and Chinese: An ERP study of Chinese bilinguals

Computational and statistical methods for analyzing event-related potential data

Contact Info

Product

Resources

About