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Volume 9
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10.3390/electronics9050775
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Article

Driving Drowsiness Detection with EEG Using a Modified Hierarchical Extreme Learning Machine Algorithm with Particle Swarm Optimization: A Pilot Study

by
Yuliang Ma
1,2,
Songjie Zhang
3,
Donglian Qi
3,
Zhizeng Luo
1,
Rihui Li
4,
Thomas Potter
4 and
Yingchun Zhang
4,*
1
Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou 310018, China
2
Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China
3
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
4
Department of Biomedical Engineering, University of Houston, Houston, TX 77204, USA
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(5), 775; https://doi.org/10.3390/electronics9050775
Submission received: 25 March 2020 / Revised: 27 April 2020 / Accepted: 28 April 2020 / Published: 8 May 2020
(This article belongs to the Section Bioelectronics)
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Abstract

Driving fatigue accounts for a large number of traffic accidents in modern life nowadays. It is therefore of great importance to reduce this risky factor by detecting the driver’s drowsiness condition. This study aimed to detect drivers’ drowsiness using an advanced electroencephalography (EEG)-based classification technique. We first collected EEG data from six healthy adults under two different awareness conditions (wakefulness and drowsiness) in a virtual driving experiment. Five different machine learning techniques, including the K-nearest neighbor (KNN), support vector machine (SVM), extreme learning machine (ELM), hierarchical extreme learning machine (H-ELM), and the proposed modified hierarchical extreme learning machine algorithm with particle swarm optimization (PSO-H-ELM), were applied to classify the subject’s drowsiness based on the power spectral density (PSD) feature extracted from the EEG data. The mean accuracies of the five classifiers were 79.31%, 79.31%, 74.08%, 81.67%, and 83.12%, respectively, demonstrating the superior performance of our new PSO-H-ELM algorithm in detecting drivers’ drowsiness, compared to the other techniques.
Keywords: drivers’ drowsiness; electroencephalography; extreme learning machines; particle swarm optimization drivers’ drowsiness; electroencephalography; extreme learning machines; particle swarm optimization

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MDPI and ACS Style

Ma, Y.; Zhang, S.; Qi, D.; Luo, Z.; Li, R.; Potter, T.; Zhang, Y. Driving Drowsiness Detection with EEG Using a Modified Hierarchical Extreme Learning Machine Algorithm with Particle Swarm Optimization: A Pilot Study. Electronics 2020, 9, 775. https://doi.org/10.3390/electronics9050775

AMA Style

Ma Y, Zhang S, Qi D, Luo Z, Li R, Potter T, Zhang Y. Driving Drowsiness Detection with EEG Using a Modified Hierarchical Extreme Learning Machine Algorithm with Particle Swarm Optimization: A Pilot Study. Electronics. 2020; 9(5):775. https://doi.org/10.3390/electronics9050775

Chicago/Turabian Style

Ma, Yuliang, Songjie Zhang, Donglian Qi, Zhizeng Luo, Rihui Li, Thomas Potter, and Yingchun Zhang. 2020. "Driving Drowsiness Detection with EEG Using a Modified Hierarchical Extreme Learning Machine Algorithm with Particle Swarm Optimization: A Pilot Study" Electronics 9, no. 5: 775. https://doi.org/10.3390/electronics9050775

APA Style

Ma, Y., Zhang, S., Qi, D., Luo, Z., Li, R., Potter, T., & Zhang, Y. (2020). Driving Drowsiness Detection with EEG Using a Modified Hierarchical Extreme Learning Machine Algorithm with Particle Swarm Optimization: A Pilot Study. Electronics, 9(5), 775. https://doi.org/10.3390/electronics9050775

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