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Classification of Ocular Artifacts in EEG Signals Using Hierarchical Clustering and Case-based Reasoning

Fulltext:


Publication Type:

Conference/Workshop Paper

Venue:

Workshop on Synergies between CBR and Data Mining at 22nd International Conference on Case-Based Reasoning


Abstract

Analysis of Electroencephalograms (EEG) recordings is becoming an important research area. However, if the signal is contaminated with noises or artifacts then it could mislead the diagnosis result. Therefore, it is important to remove artifacts from the EEG signal. This paper presents a classification approach to detect ocular artifact in the EEG signal. The proposed approach combines several methods i.e., case-based reasoning (CBR), Hierarchical clustering and Independent component analysis. The results show that the proposed system can classify EEG signal and ocular artifacts 95% accurately.

Bibtex

@inproceedings{Barua3662,
author = {Shaibal Barua and Shahina Begum and Mobyen Uddin Ahmed and Peter Funk},
title = {Classification of Ocular Artifacts in EEG Signals Using Hierarchical Clustering and Case-based Reasoning},
month = {September},
year = {2014},
booktitle = {Workshop on Synergies between CBR and Data Mining at 22nd International Conference on Case-Based Reasoning},
url = {http://www.es.mdh.se/publications/3662-}
}