Literature DB >> 21034784

A test of four EOG correction methods using an improved validation technique.

Trieu T H Pham1, Rodney J Croft, Peter J Cadusch, Robert J Barry.   

Abstract

A group of methods that are employed for removing ocular artifact from the electroencephalogram (EEG) is referred to as electrooculogram (EOG) correction methods. These use least-square linear regression, and the relative success of these is yet to be established. Improving on previous EOG correction validation studies, we present a new validation technique (with greater face validity) and use it to compare four commonly employed EOG correction methods. Data consisted of ERP traces to auditory stimuli that were embedded in up, down, left and right eye movements (EMs), recorded from 24 subjects. A 'Peak Difference' validation measure was employed, which determined the magnitude of the difference of two auditory N100 peaks (those associated with EMs with opposing polarities). All correction methods produced data that was better than not correcting at all. EOG correction methods that accounted for vertical EM, horizontal EM and blink artifact separately using separate EOG channels, produced the best corrections, with some further advantage in methods that enhanced signal (EOG) to noise (EEG) ratios when calculating correction coefficients.
Copyright © 2010 Elsevier B.V. All rights reserved.

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Year:  2010        PMID: 21034784     DOI: 10.1016/j.ijpsycho.2010.10.008

Source DB:  PubMed          Journal:  Int J Psychophysiol        ISSN: 0167-8760            Impact factor:   2.997


  4 in total

1.  Single Channel EEG Artifact Identification Using Two-Dimensional Multi-Resolution Analysis.

Authors:  Mojtaba Taherisadr; Omid Dehzangi; Hossein Parsaei
Journal:  Sensors (Basel)       Date:  2017-12-13       Impact factor: 3.576

2.  Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA-WT during Working Memory Tasks.

Authors:  Noor Kamal Al-Qazzaz; Sawal Hamid Bin Mohd Ali; Siti Anom Ahmad; Mohd Shabiul Islam; Javier Escudero
Journal:  Sensors (Basel)       Date:  2017-06-08       Impact factor: 3.576

3.  Is Brain Dynamics Preserved in the EEG After Automated Artifact Removal? A Validation of the Fingerprint Method and the Automatic Removal of Cardiac Interference Approach Based on Microstate Analysis.

Authors:  Gabriella Tamburro; Pierpaolo Croce; Filippo Zappasodi; Silvia Comani
Journal:  Front Neurosci       Date:  2021-01-12       Impact factor: 4.677

4.  Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE)-A Novel ICA-Based Algorithm for Removing Myoelectric Artifacts From EEG.

Authors:  Yongcheng Li; Po T Wang; Mukta P Vaidya; Robert D Flint; Charles Y Liu; Marc W Slutzky; An H Do
Journal:  Front Neurosci       Date:  2021-01-15       Impact factor: 4.677

  4 in total

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