You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

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

Fulltext:


Authors:

Shaibal Barua, Shahina Begum, Mobyen Uddin Ahmed, Peter Funk

Research group:

Artificial Intelligence and Intelligent Systems

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.mdu.se/publications/3662-}
}