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Intelligent automated eeg artifacts handling using wavelet transform, independent component analysis and hierarchal clustering
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-7305-7169
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1212-7637
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
2017 (English)In: Lect. Notes Inst. Comput. Sci. Soc. Informatics Telecommun. Eng., Springer Verlag , 2017, 144-148 p.Conference paper, Published paper (Refereed)
Abstract [en]

Billions of interconnected neurons are the building block of the 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 signals, recorded signals often contaminate with undesired physiological signals other than the cerebral signal that is referred to as the EEG artifacts such as the ocular or the muscle artifacts. Therefore, identification and handling of artifacts in the EEG signals in a proper way is becoming an important research area. This paper presents an automated EEG artifacts handling approach, combining Wavelet transform, Independent Component Analysis (ICA), and Hierarchical clustering. The effectiveness of the proposed approach has been examined and observed on real EEG recording. According to the result, the proposed approach identified artifacts in the EEG signals effectively and after handling artifacts EEG signals showed acceptable considering visual inspection. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.

Place, publisher, year, edition, pages
Springer Verlag , 2017. 144-148 p.
Keyword [en]
Electroencephalogram (EEG), Hierarchical clustering, Muscle artifacts, Ocular artifacts, Brain, Health care, Independent component analysis, Mobile telecommunication systems, Muscle, Wavelet transforms, Electrical signal, Electro-encephalogram (EEG), Hier-archical clustering, Independent component analysis(ICA), Physiological signals, Recorded signals, Visual inspection, Electroencephalography
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:mdh:diva-36063DOI: 10.1007/978-3-319-58877-3_19Scopus ID: 2-s2.0-85020877843ISBN: 9783319588766 (print)OAI: oai:DiVA.org:mdh-36063DiVA: diva2:1120642
Conference
14 November 2016 through 16 November 2016
Available from: 2017-07-06 Created: 2017-07-06 Last updated: 2017-07-06Bibliographically approved

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Barua, ShaibalBegum, ShahinaAhmed, Mobyen Uddin

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  • apa
  • ieee
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  • de-DE
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  • nn-NO
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  • sv-SE
  • Other locale
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Output format
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  • asciidoc
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