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Automated EEG Artifact Handling with Application in Driver Monitoring
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: 0000-0003-3802-4721
MFT, Linköping Sweden.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1212-7637
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2017 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. PP, no 99Article in journal (Refereed) Epub ahead of print
Abstract [en]

Automated analyses of electroencephalographic (EEG) signals acquired in naturalistic environments is becoming increasingly important in areas such as brain computer interfaces and behaviour science. However, the recorded EEG in such environments is often heavily contaminated by motion artifacts and eye movements. This poses new requirements on artifact handling. The objective of this paper is to present an automated EEG artifacts handling algorithm which will be used as a pre-processing step in a driver monitoring application. The algorithm, named ARTE (Automated aRTifacts handling in EEG), is based on wavelets, independent component analysis and hierarchical clustering. The algorithm is tested on a dataset obtained from a driver sleepiness study including 30 drivers and 540 30-minute 30-channel EEG recordings. The algorithm is evaluated by a clinical neurophysiologist, by quantitative criteria (signal quality index, mean square error, relative error and mean absolute error), and by demonstrating its usefulness as a preprocessing step in driver monitoring, here exemplified with driver sleepiness classification. All results are compared with a state of the art algorithm called FORCe. The quantitative and expert evaluation results show that the two algorithms are comparable and that both algorithms significantly reduce the impact of artifacts in recorded EEG signals. When artifact handling is used as a pre-processing step in driver sleepiness classification, the classification accuracy increased by 5% when using ARTE and by 2% when using FORCe. The advantage with ARTE is that it is data driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered.

Place, publisher, year, edition, pages
IEEE, 2017. Vol. PP, no 99
Keywords [en]
Artifacts, Clustering, Electroencephalogram, Independent Component Analysis, Wavelet decomposition
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:mdh:diva-37347DOI: 10.1109/JBHI.2017.2773999ISI: 000441795800003Scopus ID: 2-s2.0-85035807991OAI: oai:DiVA.org:mdh-37347DiVA, id: diva2:1160592
Projects
VDM - Vehicle Driver MonitoringSafeDriver: A Real Time Driver's State Monitoring and Prediction SystemAvailable from: 2017-11-27 Created: 2017-11-27 Last updated: 2018-09-06Bibliographically approved

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Barua, ShaibalAhmed, Mobyen UddinBegum, ShahinaFunk, Peter

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