Principal component analysis of event-related potentials: Simulation studies demonstrate misallocation of variance across components

1984 ◽  
Vol 59 (3) ◽  
pp. 249-260 ◽  
Author(s):  
Charles C. Wood ◽  
Gregory McCarthy
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Event Related Potentials ◽  
Component Analysis ◽  
Simulation Studies ◽  
Related Potentials

scholarly journals On Optimal Allocation of Treatment/Condition Variance in Principal Component Analysis

2018 ◽  
Vol 7 (4) ◽  
pp. 50
Author(s):  
André Beauducel ◽  
Norbert Hilger
Keyword(s):  
Principal Component Analysis ◽  
Treatment Condition ◽  
Optimal Allocation ◽  
Principal Component ◽  
Event Related Potentials ◽  
Component Analysis ◽  
Necessary Condition ◽  
Condition Effect ◽  
Loading Patterns ◽  
Related Potentials

The allocation of a (treatment) condition-effect on the wrong principal component (misallocation of variance) in principal component analysis (PCA) has been addressed in research on event-related potentials of the electroencephalogram. However, the correct allocation of condition-effects on PCA components might be relevant in several domains of research. The present paper investigates whether different loading patterns at each condition-level are a basis for an optimal allocation of between-condition variance on principal components. It turns out that a similar loading shape at each condition-level is a necessary condition for an optimal allocation of between-condition variance, whereas a similar loading magnitude is not necessary.

A Preliminary Investigation of ERP Components of Attentional Bias in Anxious Adults Using Temporospatial Principal Component Analysis

2021 ◽  
pp. 1-14
Author(s):  
Resh S. Gupta ◽  
Autumn Kujawa ◽  
David R. Vago
Keyword(s):  
Principal Component Analysis ◽  
Attentional Bias ◽  
Preliminary Investigation ◽  
Principal Component ◽  
Event Related Potentials ◽  
Component Analysis ◽  
Dot Probe ◽  
Face Pair ◽  
Related Potentials ◽  
Angry Face

Abstract. Threat-related attention bias is thought to contribute to the development and maintenance of anxiety disorders. Dot-probe studies using event-related potentials (ERPs) have indicated that several early ERP components are modulated by threatening and emotional stimuli in anxious populations, suggesting enhanced allocation of attention to threat and emotion at earlier stages of processing. However, ERP components selected for examination and analysis in these studies vary widely and remain inconsistent. The present study used temporospatial principal component analysis (PCA) to systematically identify ERP components elicited to face pair cues and probes in a dot-probe task in anxious adults. Cue-locked components sensitive to emotion included an early occipital C1 component enhanced for happy versus angry face pair cues and an early parieto-occipital P1 component enhanced for happy versus angry face pair cues. Probe-locked components sensitive to congruency included a parieto-occipital P2 component enhanced for incongruent probes (probes replacing neutral faces) versus congruent probes (probes replacing emotional faces). Split-half correlations indicated that the mean value around the PCA-derived peaks was reliably measured in the ERP waveforms. These results highlight promising neurophysiological markers for attentional bias research that can be extended to designs comparing anxious and healthy comparison groups. Results from a secondary exploratory PCA analysis investigating the effects of emotional face position and analyses on behavioral reaction time data are also presented.

scholarly journals Artifact Removing From Eeg Recordings Using Independent Component Analysis with High-order Statistics

2021 ◽  
Vol 15 ◽  
pp. 76-85
Author(s):  
Theodor D. Popescu
Keyword(s):  
Principal Component Analysis ◽  
Eye Movements ◽  
Independent Component Analysis ◽  
Principal Component ◽  
Event Related Potentials ◽  
Component Analysis ◽  
Independent Component ◽  
Related Potentials ◽  
Eeg Recordings ◽  
High Order Statistics

Many methods have been proposed to remove artifacts from EEG recordings especially those arising from eye movements and blinks. Often regression in time and frequency domain on parallel EEG and electrooculographic recordings is used, but this approach can become problematic in some cases. Use of Principal Component Analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. This method is not effective when the activations from cerebral activity and artifacts have comparable amplitudes. In this paper it is presented a generally applicable method for removing a wide variety of artifacts from EEG recordings based on Independent Component Analysis (ICA) with highorder statistics. The method is applied with good results in the analysis of a sample lowpass event -related potentials (ERP) data.

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