Recovering dipole sources from scalp-recorded event-related-potentials using component analysis: principal component analysis and independent component analysis

2004 ◽  
Vol 54 (3) ◽  
pp. 201-220 ◽  
Author(s):  
John E. Richards
Keyword(s):  
Principal Component Analysis ◽  
Independent Component Analysis ◽  
Principal Component ◽  
Event Related Potentials ◽  
Component Analysis ◽  
Independent Component ◽  
Related Potentials ◽  
Dipole Sources

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.

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.

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