Independent component analysis of event-related potentials during a selective attention task

1996 ◽  
Author(s):  
S. Makeig ◽  
L. Anllo-Vento ◽  
P. Jung ◽  
A. J. Bell ◽  
T. J. Sejnowski ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Selective Attention ◽  
Event Related Potentials ◽  
Component Analysis ◽  
Independent Component ◽  
Attention Task ◽  
Related Potentials

Improving Accuracy of Event-Related Potentials Classification by Channel Selection Using Independent Component Analysis and Least Square Methods

2016 ◽  
Vol 8 (3) ◽  
pp. 1-18 ◽  
Author(s):  
Wenxuan Li ◽  
Mengfan Li ◽  
Wei Li
Keyword(s):  
Independent Component Analysis ◽  
Event Related Potentials ◽  
Channel Selection ◽  
Component Analysis ◽  
Independent Component ◽  
Least Square ◽  
Individual Subject ◽  
Combination Methods ◽  
Related Potentials ◽  
Channel Combination

This paper proposes a method for achieving a high performance of N200 and P300 classification by applying independent component analysis to select the channels, which deliver brain signals with large N200 and P300 potentials and small artifacts. In this study, the authors find out the relationship between the highest accuracy and the weights of the independent components and use this relationship to predict the optimal channels of each individual subject. They compare five channel selection methods: the ICA-based method and the curve-fitting-based method proposed in this paper, the amplitude-based method, the experiential optimal 8 channel combination and all 30 channel combination methods. The comparative studies show that the ICA-based method achieves an average accuracy of 99.3% across four subjects, which is superior to the other four methods.

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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