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A Review on Machine Learning Algorithms in Handling EEG Artifacts
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
2014 (English)In: The Swedish AI Society (SAIS) Workshop SAIS, 14, 2014Conference paper (Refereed)
Abstract [en]

Brain waves obtained by Electroencephalograms (EEG) recording are an important research area in medical and health and brain computer interface (BCI). Due to the nature of EEG signal, noises and artifacts can contaminate it, which leads to a serious misinterpretation in EEG signal analysis. These contaminations are referred to as artifacts, which are signals of other than brain activity. Moreover, artifacts can cause significant miscalculation of the EEG measurements that reduces the clinical usefulness of EEG signals. Therefore, artifact handling is one of the cornerstones in EEG signal analysis. This paper provides a review of machine learning algorithms that have been applied in EEG artifacts handling such as artifacts identification and removal. In addition, an analysis of these methods has been reported based on their performance.

Place, publisher, year, edition, pages
Keyword [en]
Electroencephalograms (EEG), Artifacts, Machine Learning
National Category
Engineering and Technology
URN: urn:nbn:se:mdh:diva-26427OAI: diva2:759967
The Swedish AI Society (SAIS) Workshop SAIS, 14, 22-23 May 2014, Stockholm, Sweden
VDM - Vehicle Driver Monitoring
Available from: 2014-11-01 Created: 2014-10-31 Last updated: 2015-10-06Bibliographically 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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