Wireless Mobile Communication and Healthcare. 6th International Conference, MobiHealth 2016, Milan, Italy, November 14-16, 2016, Proceedings

Research Article

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

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  • @INPROCEEDINGS{10.1007/978-3-319-58877-3_19,
        author={Shaibal Barua and Shahina Begum and Mobyen Ahmed},
        title={Intelligent Automated EEG Artifacts Handling Using Wavelet Transform, Independent Component Analysis and Hierarchal Clustering},
        proceedings={Wireless Mobile Communication and Healthcare. 6th International Conference, MobiHealth 2016, Milan, Italy, November 14-16, 2016, Proceedings},
        proceedings_a={MOBIHEALTH},
        year={2017},
        month={6},
        keywords={Electroencephalogram (EEG) Ocular artifacts Muscle artifacts Hierarchical clustering},
        doi={10.1007/978-3-319-58877-3_19}
    }
    
  • Shaibal Barua
    Shahina Begum
    Mobyen Ahmed
    Year: 2017
    Intelligent Automated EEG Artifacts Handling Using Wavelet Transform, Independent Component Analysis and Hierarchal Clustering
    MOBIHEALTH
    Springer
    DOI: 10.1007/978-3-319-58877-3_19
Shaibal Barua1,*, Shahina Begum1,*, Mobyen Ahmed1,*
  • 1: Mälardalen University
*Contact email: shaibal.barua@mdh.se, shahina.begum@mdh.se, mobyen.ahmed@mdh.se

Abstract

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.