scholarly journals Using Temporally Aligned Event-Related Potentials to Investigate Attention Shifts Before and During Eye Movements

2016 ◽  
Vol 16 (12) ◽  
pp. 613
Author(s):  
Christoph Huber-Huber ◽  
Thomas Ditye ◽  
Maria Marchante ◽  
Ulrich Ansorge
Keyword(s):  
Eye Movements ◽  
Event Related Potentials ◽  
Related Potentials ◽  
Attention Shifts

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

2021 ◽  
Vol 15 ◽  
pp. 1336-1345
Author(s):  
Theodor D. Popescu
Keyword(s):  
Eye Movements ◽  
Independent Component Analysis ◽  
Order Statistics ◽  
Event Related Potentials ◽  
Component Analysis ◽  
High Order ◽  
Dependent Component ◽  
Related Potentials ◽  
Eeg Recordings ◽  
High Order Statistics

Many methods have been proposed to remove artifacts from EEG recordings es- pecially 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 Com- ponent Analysis (PCA) has been proposed to re- move 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 gener- ally applicable method for removing a wide vari- ety of artifacts from EEG recordings based on In- dependent Component Analysis (ICA) with high- order statistics. The method is applied with good results in the analysis of a sample lowpass event -related potentials (ERP) data.

Event-related potentials in idiopathic rapid eye movements sleep behaviour disorder

2007 ◽  
Vol 118 (3) ◽  
pp. 669-675 ◽  
Author(s):  
Alberto Raggi ◽  
Mauro Manconi ◽  
Monica Consonni ◽  
Cristina Martinelli ◽  
Marco Zucconi ◽  
...  
Keyword(s):  
Eye Movements ◽  
Event Related Potentials ◽  
Behaviour Disorder ◽  
Rapid Eye Movements ◽  
Sleep Behaviour ◽  
Related Potentials

scholarly journals EEG based Spatial Attention Shifts Detection using Time-Frequency features on Empirical Wavelet Transform

2021 ◽  
pp. 144-149
Author(s):  
Gokhan Altan ◽  
Gulcin Inat
Keyword(s):  
Wavelet Transform ◽  
Spatial Attention ◽  
Event Related Potentials ◽  
Frequency Domain Analysis ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Time Frequency ◽  
Empirical Wavelet Transform ◽  
Related Potentials ◽  
Attention Shifts

The human nervous system has over 100b nerve cells, of which the majority are located in the brain. Electrical alterations, Electroencephalogram (EEG), occur through the interaction of the nerves. EEG is utilized to evaluate event-related potentials, imaginary motor tasks, neurological disorders, spatial attention shifts, and more. In this study, We experimented with 29-channel EEG recordings from 18 healthy individuals. Each recording was decomposed using Empirical Wavelet Transform, a time-frequency domain analysis technique at the feature extraction stage. The statistical features of the modulations were calculated to feed the conventional machine learning algorithms. The proposal model achieved the best spatial attention shifts detection accuracy using the Decision Tree algorithm with a rate of 89.24%.

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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