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• W3157239432 endingPage "13980" @default.
• W3157239432 startingPage "13965" @default.
• W3157239432 abstract "Vehicle drivers driving cars under the situation of drowsiness can cause serious traffic accidents. In this paper, a vehicle driver drowsiness detection method using wearable electroencephalographic (EEG) based on convolution neural network (CNN) is proposed. The presented method consists of three parts: data collection using wearable EEG, vehicle driver drowsiness detection and the early warning strategy. Firstly, a wearable brain computer interface (BCI) is used to monitor and collect the EEG signals in the simulation environment of drowsy driving and awake driving. Secondly, the neural networks with Inception module and modified AlexNet module are trained to classify the EEG signals. Finally, the early warning strategy module will function and it will sound an alarm if the vehicle driver is judged as drowsy. The method was tested on driving EEG data from simulated drowsy driving. The results show that using neural network with Inception module reached 95.59% classification accuracy based on one second time window samples and using modified AlexNet module reached 94.68%. The simulation and test results demonstrate the feasibility of the proposed drowsiness detection method for vehicle driving safety." @default.
• W3157239432 created "2021-05-10" @default.
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• W3157239432 date "2021-05-04" @default.
• W3157239432 modified "2023-10-15" @default.
• W3157239432 title "Vehicle driver drowsiness detection method using wearable EEG based on convolution neural network" @default.
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• W3157239432 doi "https://doi.org/10.1007/s00521-021-06038-y" @default.
• W3157239432 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8093370" @default.
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• W3157239432 hasPublicationYear "2021" @default.
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