Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81642
Title: A WPCA-based method for detecting fatigue driving from EEG-based internet of vehicles system
Authors: Dong, N
Li, YJ
Gao, ZK
Ip, WH 
Yung, KL 
Keywords: AR model
Driving fatigue detection
Feature reduction
Internet of Vehicles
WPCA algorithm
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE access, 2019, v. 7, p. 124702-124711 How to cite?
Journal: IEEE access 
Abstract: Fatigue driving is the main cause of traffic accidents. Analysis of electroencephalogram (EEG) signals has attracted wide attention for identifying fatigue driving. With the development of the Internet of Vehicles (IoV), we hope to establish an EEG-based IoV traffic management system to improve traffic safety. In the proposed system, real-time diagnosis is a significant factor, and improvement of the detection speed is our main concern. EEG signals generate a large amount of spatially oriented data over a relatively short duration; hence, their dimension needs to be reduced effectively before being analysed. We proposes a feature reduction method, based on a novel weighted principal component analysis (WPCA) algorithm for EEG signals. First, the EEG features are extracted by an autoregressive (AR) model. Second, we calculate the influence of different features on the classified performance of fatigue state. The accuracy reduction values of different features are normalised as the weights of the features. Finally, these weights are assigned to the WPCA to reduce the EEG features. To verify the effectiveness of the algorithm, we carried out a simulated driving experiment involving eight participants. For comparison, power spectral density and differential entropy models were also introduced to extract EEG features. Support Vector Machine was adopted as a classifier to establish a fatigue driving classification experiment. The experimental results show that the WPCA method can effectively reduce the feature dimension of different EEG feature extraction methods, speed up calculations, and achieve a much higher classification accuracy of fatigue driving.
URI: http://hdl.handle.net/10397/81642
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2937914
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
The following publication N. Dong, Y. Li, Z. Gao, W. H. Ip and K. L. Yung, "A WPCA-Based Method for Detecting Fatigue Driving From EEG-Based Internet of Vehicles System," in IEEE Access, vol. 7, pp. 124702-124711, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2937914
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