A WPCA-based method for detecting fatigue driving from EEG-based internet of vehicles system

Na Dong, Yingjie Li, Zhongke Gao, Wai Hung Ip, Kai Leung Yung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)

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.

Original languageEnglish
Article number8815722
Pages (from-to)124702-124711
Number of pages10
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 27 Aug 2019

Keywords

  • AR model
  • driving fatigue detection
  • feature reduction
  • Internet of Vehicles
  • WPCA algorithm

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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