Automated EEG Artifact Handling With Application in Driver Monitoring

• Published in: IEEE journal of biomedical and health informatics Vol. 22; no. 5; pp. 1350 - 1361
• Main Authors: Barua, Shaibal, Ahmed, Mobyen Uddin, Ahlstrom, Christer, Begum, Shahina, Funk, Peter
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 84 Road: Road users; 914 Road: ITS och vehicle technology; Algorithm design and analysis; Algorithms; Analysis (math); Automatic; Automation; Brain; Classification; Cluster analysis; Clustering; Clustering algorithms; Continuous; Data processing; Driver; Driver fatigue; EEG; Electroencephalogram; Electroencephalography; Errors; Eye movements; Feature extraction; Independent Component Analysis; Interfaces; Materials handling; Mathematical model; Measurement; Monitoring; Muscles; Preprocessing; Reference signals; Signal quality; Sleep; Sleep and wakefulness; Sleepiness; Vehicles; Wavelet analysis; Wavelet decomposition
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2017.2773999

Automated analyses of electroencephalographic (EEG) signals acquired in naturalistic environments are becoming increasingly important in areas such as brain-computer interfaces and behavior 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 preprocessing step in a driver monitoring application. The algorithm, named Automated aRTifacts handling in EEG (ARTE), 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-min 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 preprocessing 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.