TY - JOUR
T1 - Driving drowsiness detection with EEG using a modified hierarchical extreme learning machine algorithm with particle swarm optimization
T2 - A pilot study
AU - Ma, Yuliang
AU - Zhang, Songjie
AU - Qi, Donglian
AU - Luo, Zhizeng
AU - Li, Rihui
AU - Potter, Thomas
AU - Zhang, Yingchun
N1 - Funding Information:
This research was funded by the National Natural Science Foundation of China grant number 6137202361671197 And the APC was funded by 61372023.
Funding Information:
Funding: This research was funded by the National Natural Science Foundation of China grant number 61372023, 61671197 And the APC was funded by 61372023.
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020/5
Y1 - 2020/5
N2 - 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.
AB - 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.
KW - Drivers’ drowsiness
KW - Electroencephalography
KW - Extreme learning machines
KW - Particle swarm optimization
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U2 - 10.3390/electronics9050775
DO - 10.3390/electronics9050775
M3 - Article
AN - SCOPUS:85084535356
VL - 9
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
SN - 2079-9292
IS - 5
M1 - 775
ER -