EP Component Identification and Measurement by Principal Components-Analysis

Chapman, Robert M.; McCrary, John W.

doi:10.1006/brcg.1995.1024

Cited by 208 publications

(111 citation statements)

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“…Principal components of data generated by temporally sparse and independent, but spatially nonorthogonal, sources will be linear combinations of activity in all the sources, whereas ICA components of the data will individually identify the larger sources (28). The proposed Varimax extension of the PCA method rotates the PCA vectors to maximize the variance of their activation waveforms (4). However, the relevance of this criterion to ERP genesis is unclear.…”

Section: Resultsmentioning

confidence: 99%

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Blind separation of auditory event-related brain responses into independent components

Makeig

Jung

Bell

et al. 1997

Proc. Natl. Acad. Sci. U.S.A.

1,059

652

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Averaged event-related potential (ERP) data recorded from the human scalp reveal electroencephalographic (EEG) activity that is reliably time-locked and phaselocked to experimental events. We report here the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely activated, maximally independent time courses. Independent component analysis (ICA) decomposes ERP data into a number of components equal to the number of sensors. The derived components have distinct but not necessarily orthogonal scalp projections. Unlike dipole-fitting methods, the algorithm does not model the locations of their generators in the head. Unlike methods that remove second-order correlations, such as principal component analysis (PCA), ICA also minimizes higherorder dependencies. Applied to detected-and undetectedtarget ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions. Three of these components were active only when the subject detected the targets, three other components only when the target went undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. Major features of the decomposition proved robust across sessions and changes in sensor number and placement. This method of ERP analysis can be used to compare responses from multiple stimuli, task conditions, and subject states.Although the locations of the brain areas generating eventrelated potentials (ERPs) cannot be uniquely determined by scalp recordings from any number of channels (1), several methods have been proposed for decomposing evoked responses into activations of distinct neural sources. Most of these also attempt to locate the active areas, by assuming either that they have a known or simple spatial configuration (2) or that generators are restricted to a small subset of possible locations and orientations (3). Other methods based on rotations of principal components use optimization criteria not directly related to brain anatomy and physiology. These methods may assume that each response component has the same time course of activation in every experimental condition (4). All these methods use second-order spatiotemporal correlations to perform the decomposition.Here we report a statistical method for decomposing one or more event-related brain responses into a sum of components with spatially fixed scalp distributions and maximally independent (though possibly overlapping) time courses. Independence requires the absence of higher-order as well as secondorder correlations between the time courses. Independence, therefore, is a stronger condition than decorrelation and, in particular, is not satisfied by decomposition into principal components by principal component analysis (PCA).Although the ...

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Section: Resultsmentioning

confidence: 99%

“…Other methods based on rotations of principal components use optimization criteria not directly related to brain anatomy and physiology. These methods may assume that each response component has the same time course of activation in every experimental condition (4). All these methods use second-order spatiotemporal correlations to perform the decomposition.…”

mentioning

confidence: 99%

Blind separation of auditory event-related brain responses into independent components

Makeig

Jung

Bell

et al. 1997

Proc. Natl. Acad. Sci. U.S.A.

1,059

652

View full text Add to dashboard Cite

show abstract

“…Because the ERP itself is a multivariate observation (due to its many post-stimulus time samples), we applied a multivariate measurement method, Varimaxed Principal Components Analysis (PCA) (Chapman and McCrary, 1995; Dien, 1998; Kayser and Tenke, 2005; Picton et al, 2000), to identify and measure the latent components of the ERPs. Volume conduction in the brain suggests an additive ERP model, which underlies the PCA process in extracting the component structure (Chapman and McCrary, 1995).…”

Section: Methodsmentioning

confidence: 99%

“…Often ERP components overlap in time, particularly in this early post-stimulus period when a large amount of processing occurs including sensory and perceptual functions. A formal multivariate method such as Principal Components Analysis (PCA) allows for extraction of individual components in a parsimonious fashion that requires neither making strong assumptions about the nature of those components nor identifying particular time regions of interest (Chapman and McCrary, 1995). Differentiating ERP components by measuring peaks or broad time regions in averaged ERPs is not conducive to detecting underlying ERP components and may produce muddied measures of these components if they occur in rapid succession.…”

Section: Introductionmentioning

confidence: 99%

Memory timeline: Brain ERP C250 (not P300) is an early biomarker of short-term storage

et al. 2015

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Brain Event-Related Potentials (ERPs) offer a quantitative link between neurophysiological activity and cognitive performance. ERPs were measured while young adults performed a task that required storing a relevant stimulus in short-term memory. Using Principal Components Analysis, ERP component C250 (maximum at 250 ms post-stimulus) was extracted from a set of ERPs that were separately averaged for various task conditions, including stimulus relevancy and stimulus sequence within a trial. C250 was more positive in response to task-specific stimuli that were successfully stored in short-term memory. This relationship between C250 and short-term memory storage of a stimulus was confirmed by a memory probe recall test where the behavioral recall of a stimulus was highly correlated with its C250 amplitude. ERP component P300 (and its subcomponents of P3a and P3b, which are commonly thought to represent memory operations) did not show a pattern of activation reflective of storing task-relevant stimuli. C250 precedes the P300, indicating that initial short-term memory storage may occur earlier than previously believed. Additionally, because C250 is so strongly predictive of a stimulus being stored in short-term memory, C250 may provide a strong index of early memory operations.

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“…These components corresponded to the areas of maximal variability in the waveform, such as slopes or peaks. Although questions regarding the possibility of misallocation of variance in a PCA analysis across immediately adjacent components have been raised in the past (e.g., Wood and McCarthy 1984), even Wood and McCarthy (1984, p. 258) noted that traditional amplitude and latency approaches are “no less subject to the problem of component overlap” (see also Chapman and McCrary 1995 and Beauducel and Debener 2003 for more recent treatments of this discussion).…”

Section: Methodsmentioning

confidence: 99%

Smoking during Pregnancy Affects Speech-Processing Ability in Newborn Infants

Key

Ferguson

Molfese

et al. 2007

Environ Health Perspect

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BackgroundTobacco smoking during pregnancy is known to adversely affect development of the central nervous system in babies of smoking mothers by restricting utero–placental blood flow and the amount of oxygen available to the fetus. Behavioral data associate maternal smoking with lower verbal scores and poorer performance on specific language/auditory tests.ObjectivesIn the current study we examined the effects of maternal smoking during pregnancy on newborns’ speech processing ability as measured by event-related potentials (ERPs).MethodHigh-density ERPs were recorded within 48 hr of birth in healthy newborn infants of smoking (n = 8) and nonsmoking (n = 8) mothers. Participating infants were matched on sex, gestational age, birth weight, Apgar scores, mother’s education, and family income. Smoking during pregnancy was determined by parental self-report and medical records. ERPs were recorded in response to six consonant–vowel syllables presented in random order with equal probability.ResultsBrainwaves of babies of nonsmoking mothers were characterized by typical hemisphere asymmetries, with larger amplitudes over the left hemisphere, especially over temporal regions. Further, infants of nonsmokers discriminated among a greater number of syllables whereas the newborns of smokers began the discrimination process at least 150 msec later and differentiated among fewer stimuli.ConclusionsOur findings indicate that prenatal exposure to tobacco smoke in otherwise healthy babies is linked with significant changes in brain physiology associated with basic perceptual skills that could place the infant at risk for later developmental problems.

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EP Component Identification and Measurement by Principal Components-Analysis

Cited by 208 publications

References 0 publications

Blind separation of auditory event-related brain responses into independent components

Blind separation of auditory event-related brain responses into independent components

Memory timeline: Brain ERP C250 (not P300) is an early biomarker of short-term storage

Smoking during Pregnancy Affects Speech-Processing Ability in Newborn Infants

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