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A machine learning approach to artefact extraction from independent components derived from EEG datasets

Revett, Kenneth and Gorunescu, Florin and El-Dahshan, El-Sayed and Salem, Abdel-Badeeh M. (2009) A machine learning approach to artefact extraction from independent components derived from EEG datasets. In: Fourth International Conference on Intelligent Computing and Information Systems (ICICIS 2009), 18 - 22 Mar 2009, Cairo, Egypt.

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Abstract

Artefact selection from EEG datasets is still a task remanded to domain experts. In this study, a working memory task dataset is used to examine automated methods for artefact removal. Such artefacts include eye blinks, muscle movements, and blood flow changes that do not reflect actual physiological responses to presented stimuli. In this work, a set of attributes were extracted from a 33 channel EEG recording. The attributes related predominantly to the independent components that were generated from the epoched data. In conjunction with expert analysis of the components, the attributes were used to produce an automated component artefact removal system. The result is an artefact removal system that performs at essentially 94% accuracy.

Item Type:Conference or Workshop Item (Paper)
Research Community:University of Westminster > Electronics and Computer Science, School of
ID Code:7104
Deposited On:06 Jan 2010 11:33
Last Modified:26 Jun 2012 10:03

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