Clustering based Approach for Automated EEG Artifacts Handling
2015 (English)In: Frontiers in Artificial Intelligence and Applications, vol. 278, 2015, 7-16 p.Conference paper (Refereed)
Electroencephalogram (EEG), measures the neural activity of the central nervous system, which is widely used in diagnosing brain activity and therefore plays a vital role in clinical and Brain-Computer Interface application. However, analysis of EEG signal is often complex since the signal recoding often contaminates with noises or artifacts such as ocular and muscle artifacts, which could mislead the diagnosis result. Therefore, to identify the artifacts from the EEG signal and handle it in a proper way is becoming an important and interesting research area. This paper presents an automated EEG artifacts handling approach, where it combines Independent Component Analysis (ICA) with a 2nd order clustering approach. Here, the 2nd order clustering approach combines the Hierarchical and Gaussian Picture Model clustering algorithm. 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 the clean EEG signal shows acceptable considering visual inspection.
Place, publisher, year, edition, pages
2015. 7-16 p.
Electroencephalogram (EEG), Machine-Learning, Ocular artifacts. Muscle artifacts, Clustering
Research subject Computer Science
IdentifiersURN: urn:nbn:se:mdh:diva-29094DOI: 10.3233/978-1-61499-589-0-7ScopusID: 2-s2.0-84963692293ISBN: 9781614995883OAI: oai:DiVA.org:mdh-29094DiVA: diva2:855895
The 13th Scandinavian Conference on Artificial Intelligence (SCAI 2015), 5-6 November, 2015, Halmstad, Sweden
ProjectsVDM - Vehicle Driver Monitoring