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Intelligent Automated EEG Artifacts Handling Using Wavelet Transform, Independent Component Analysis and Hierarchal clustering

Authors:

Shaibal Barua, Shahina Begum, Mobyen Uddin Ahmed

Research group:

Artificial Intelligence and Intelligent Systems

Publication Type:

Conference/Workshop Paper

Venue:

1st Workshop on Embedded Sensor Systems for Health through Internet of Things

Abstract

Billions of interconnected neurons are the building block of human brain. For each brain activity these neurons produce electrical signals or brain waves that can be obtained by the Electroencephalogram (EEG) recording. Due to the characteristics of EEG signal, recorded signal often contaminate with undesired physiological signals other than cerebral signal that refers to as EEG artifacts such as ocular or muscle artifacts. Therefore, identification of artifacts from the EEG signal and handle it in a proper way is becoming an important research area. This paper presents an automated EEG artifacts handling approach, where it combines Wavelet transform, Independent Component Analysis (ICA) with Hierarchical clustering method. The effectiveness of the proposed approach has been examined and observed on real EEG recording. According to result, the artifacts in the EEG signals are identified and removed successfully where after handling artifacts EEG signals show acceptable considering visual inspection.

Bibtex

@inproceedings{Barua4027,
author = {Shaibal Barua and Shahina Begum and Mobyen Uddin Ahmed},
title = {Intelligent Automated EEG Artifacts Handling Using Wavelet Transform, Independent Component Analysis and Hierarchal clustering},
month = {October},
year = {2015},
booktitle = {1st Workshop on Embedded Sensor Systems for Health through Internet of Things},
url = {http://www.es.mdu.se/publications/4027-}
}