Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: II. Adequacy of low-density estimates

Kayser, Jürgen; Tenke, Craig E.

doi:10.1016/j.clinph.2005.08.033

Cited by 249 publications

(213 citation statements)

References 39 publications

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“…Like microstate segmentation, PCA is essentially an exploratory and descriptive method for summarizing complex, multi-channel ERP data sets by reducing their temporal and/or spatial dimensions. Thus, PCA can provide useful insight into how ERPs components are affected by the experimental manipulations (see [61,62] for description of the PCA analysis; see [63][64][65][66][67] for additional technical details). Without any a priori assumptions about the shape or number of components in the data set, the PCA will determine the complex relationships between a large number of dependent variables (i.e., the voltage at each time frame for a temporal PCA and the voltage at each electrode for a spatial PCA) and summarize these relations in terms of unobserved dependent variables (what is usually called temporal or spatial factor in a PCA), corresponding to the recorded components.…”

Section: General Principles Of Pcamentioning

confidence: 99%

“…Thus, PCA provides a measure of the contribution of each factor to the observed ERPs, and allows subsequent tests to determine any statistical difference between conditions. The application of PCA is not limited to ERPs and can also concern raw EEG epochs (e.g., [68]), serve as a method to efficiently filter the data (e.g., ocular artefact; [69]), or be used for specific source localization purposes [66,67].…”

Section: General Principles Of Pcamentioning

confidence: 99%

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Beyond Conventional Event-related Brain Potential (ERP): Exploring the Time-course of Visual Emotion Processing Using Topographic and Principal Component Analyses

Pourtois

Delplanque

Michel³

et al. 2008

Brain Topogr

123

107

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Recent technological advances with the scalp EEG methodology allow researchers to record electric fields generated in the human brain using a large number of electrodes or sensors (e.g. 64-256) distributed over the head surface (multi-channel recording). As a consequence, such high-density ERP mapping yields fairly dense ERP data sets that are often hard to analyze comprehensively or to relate straightforwardly to specific cognitive or emotional processes, because of the richness of the recorded signal in both the temporal (millisecond time-resolution) and spatial (multidimensional topographic information) domains. Principal component analyses (PCA) and topographic analyses (combined with distributed source localization algorithms) have been developed and successfully used to deal with this complexity, now offering powerful alternative strategies for data-driven analyses in complement to more traditional ERP analyses based on waveforms and peak measures. In this paper, we first briefly review the basic principles of these approaches, and then describe recent ERP studies that illustrate how they can inform about the precise spatio-temporal dynamic of emotion processing. These studies show that the perception of emotional visual stimuli may produce both quantitative and qualitative changes in the electric field configuration recorded at the scalp level, which are not apparent when using conventional ERP analyses. Additional information gained from these approaches include the identification of a sequence of successive processing stages that may not fully be reflected in ERP waveforms only, and the segregation of multiple or partly overlapping neural events that may be blended within a single ERP waveform. These findings highlight the added value of such alternative analyses when exploring the electrophysiological manifestations of complex and distributed mental functions, as for instance during emotion processing.

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Section: General Principles Of Pcamentioning

confidence: 99%

Section: General Principles Of Pcamentioning

confidence: 99%

Beyond Conventional Event-related Brain Potential (ERP): Exploring the Time-course of Visual Emotion Processing Using Topographic and Principal Component Analyses

Pourtois

Delplanque

Michel³

et al. 2008

Brain Topogr

123

107

View full text Add to dashboard Cite

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“…The tPCA enables temporally overlapping components to be separated (Dien & Frishkoff, 2005). The combination of a Laplacian filter and tPCA has been described in depth by Kayser and Tenke (2006). A scree test was used to select from among the eight temporal factors (TFs) extracted using the covariance matrix (Promax rotation).…”

Section: Offline Processingmentioning

confidence: 99%

“…An independent component analysis algorithm was applied to remove components related to ocular artifacts. We also performed a current source density transformation based on the spherical-spline surface Laplacian, using the MATLAB code provided by Kayser and Tenke (2006) (smoothing constant = 10…”

Section: Offline Processingmentioning

confidence: 99%

When the brain simulates stopping: Neural activity recorded during real and imagined stop-signal tasks

González-Villar

Bonilla

Carrillo-de-la-Peña

2016

Cogn Affect Behav Neurosci

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It has been suggested that mental rehearsal activates brain areas similar to those activated by real performance. Although inhibition is a key function of human behavior, there are no previous reports of brain activity during imagined response cancellation. We analyzed event-related potentials (ERPs) and time-frequency data associated with motor execution and inhibition during real and imagined performance of a stop-signal task. The ERPs characteristic of stop trials-that is, the stop-N2 and stop-P3-were also observed during covert performance of the task. Imagined stop (IS) trials yielded smaller stop-N2 amplitudes than did successful stop (SS) and unsuccessful stop (US) trials, but midfrontal theta power similar to that in SS trials. The stop-P3 amplitude for IS was intermediate between those observed for SS and US. The results may be explained by the absence of error-processing and correction processes during imagined performance. For go trials, real execution was associated with higher mu and beta desynchronization over motor areas, which confirms previous reports of lower motor activation during imagined execution and also with larger P3b amplitudes, probably indicating increased top-down attention to the real task. The similar patterns of activity observed for imagined and real performance suggest that imagination tasks may be useful for training inhibitory processes. Nevertheless, brain activation was generally weaker during mental rehearsal, probably as a result of the reduced engagement of top-down mechanisms and limited error processing.

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“…The trade-off between noise attenuation and loss of spatial resolution in low-density arrays was discussed by Tenke et al (1993). In fact, lower-density estimates has proven to be quite useful more than once, as described by Kayser and Tenke (2006a) in the context of ERP analysis. Finally, we point out that evidence for electrode bridges in high-density EEG recordings was identified as a problem caused typically by electrolyte spreading between nearby electrodes (Tenke and Kayser, 2001;Greischar et al, 2004;Alschuler et al, 2014).…”

Section: Finite Difference Methodsmentioning

confidence: 99%

The surface Laplacian technique in EEG: Theory and methods

Carvalhaes¹,

Barros

2015

International Journal of Psychophysiology

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This paper reviews the method of surface Laplacian differentiation to study EEG. We focus on topics that are helpful for a clear understanding of the underlying concepts and its efficient implementation, which is especially important for EEG researchers unfamiliar with the technique. The popular methods of finite difference and splines are reviewed in detail. The former has the advantage of simplicity and low computational cost, but its estimates are prone to a variety of errors due to discretization. The latter eliminates all issues related to discretization and incorporates a regularization mechanism to reduce spatial noise, but at the cost of increasing mathematical and computational complexity. These and several others issues deserving further development are highlighted, some of which we address to the extent possible. Here we develop a set of discrete approximations for Laplacian estimates at peripheral electrodes and a possible solution to the problem of multiple-frame regularization. We also provide the mathematical details of finite difference approximations that are missing in the literature, and discuss the problem of computational performance, which is particularly important in the context of EEG splines where data sets can be very large. Along this line, the matrix representation of the surface Laplacian operator is carefully discussed and some figures are given illustrating the advantages of this approach. In the final remarks, we briefly sketch a possible way to incorporate finite-size electrodes into Laplacian estimates that could guide further developments.

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Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: II. Adequacy of low-density estimates

Cited by 249 publications

References 39 publications

Beyond Conventional Event-related Brain Potential (ERP): Exploring the Time-course of Visual Emotion Processing Using Topographic and Principal Component Analyses

Beyond Conventional Event-related Brain Potential (ERP): Exploring the Time-course of Visual Emotion Processing Using Topographic and Principal Component Analyses

When the brain simulates stopping: Neural activity recorded during real and imagined stop-signal tasks

The surface Laplacian technique in EEG: Theory and methods

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