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Clustering based Approach for Automated EEG Artifacts Handling
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
2015 (English)In: Frontiers in Artificial Intelligence and Applications, vol. 278, 2015, 7-16 p.Conference paper (Refereed)
Abstract [en]

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.
Keyword [en]
Electroencephalogram (EEG), Machine-Learning, Ocular artifacts. Muscle artifacts, Clustering
National Category
Computer Systems
Research subject
Computer Science
URN: urn:nbn:se:mdh:diva-29094DOI: 10.3233/978-1-61499-589-0-7ScopusID: 2-s2.0-84963692293ISBN: 9781614995883OAI: diva2:855895
The 13th Scandinavian Conference on Artificial Intelligence (SCAI 2015), 5-6 November, 2015, Halmstad, Sweden
VDM - Vehicle Driver Monitoring
Available from: 2015-09-22 Created: 2015-09-22 Last updated: 2016-05-17Bibliographically approved
In thesis
1. Intelligent Driver Mental State Monitoring System Using Physiological Sensor Signals
Open this publication in new window or tab >>Intelligent Driver Mental State Monitoring System Using Physiological Sensor Signals
2015 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Driving a vehicle involves a series of events, which are related to and evolve with the mental state (such as sleepiness, mental load, and stress) of the driv- er. These states are also identified as causal factors of critical situations that can lead to road accidents and vehicle crashes. These driver impairments need to be detected and predicted in order to reduce critical situations and road accidents. In the past years, physiological signals have become conven- tional measures in driver impairment research. Physiological signals have been applied in various studies to identify different levels of mental load, sleepiness, and stress during driving.

This licentiate thesis work has investigated several artificial intelligence algorithms for developing an intelligent system to monitor driver mental state using physiological signals. The research aims to measure sleepiness and mental load using Electroencephalography (EEG). EEG signals, if pro- cessed correctly and efficiently, have potential to facilitate advanced moni- toring of sleepiness, mental load, fatigue, stress etc. However, EEG signals can be contaminated with unwanted signals, i.e., artifacts. These artifacts can lead to serious misinterpretation. Therefore, this work investigates EEG arti- fact handling methods and propose an automated approach for EEG artifact handling. Furthermore, this research has also investigated how several other physiological parameters (Heart Rate (HR) and Heart Rate Variability (HRV) from the Electrocardiogram (ECG), Respiration Rate, Finger Tem- perature (FT), and Skin Conductance (SC)) to quantify drivers’ stress. Dif- ferent signal processing methods have been investigated to extract features from these physiological signals. These features have been extracted in the time domain, in the frequency domain as well as in the joint time-frequency domain using wavelet analysis. Furthermore, data level signal fusion has been proposed using Multivariate Multiscale Entropy (MMSE) analysis by combining five physiological sensor signals. Primarily Case-Based Reason- ing (CBR) has been applied for drivers’ mental state classification, but other Artificial intelligence (AI) techniques such as Fuzzy Logic, Support Vector Machine (SVM) and Artificial Neural Network (ANN) have been investigat- ed as well.

For drivers’ stress classification, using the CBR and MMSE approach, the system has achieved 83.33% classification accuracy compared to a human expert. Moreover, three classification algorithms i.e., CBR, an ANN, and a SVM were compared to classify drivers’ stress. The results show that CBR has achieved 80% and 86% accuracy to classify stress using finger tempera- ture and heart rate variability respectively, while ANN and SVM reached an accuracy of less than 80%. 

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2015
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 217
Artificial Intelligent, Intelligent systems, Physiological signal, Driver monitoring
National Category
Computer Science
Research subject
Computer Science
urn:nbn:se:mdh:diva-28902 (URN)978-91-7485-231-8 (ISBN)
2015-10-06, Lambda, Mälardalens högskola, Västerås, 13:15 (English)
Vehicle Driver Monitoring
Available from: 2015-09-11 Created: 2015-09-10 Last updated: 2015-09-22Bibliographically approved

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