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Wavelet Packet Transform Based Driver Distraction Level Classification Using EEG

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  • Mousa Kadhim Wali
  • Murugappan Murugappan
  • Badlishah Ahmmad

Abstract

We classify the driver distraction level (neutral, low, medium, and high) based on different wavelets and classifiers using wireless electroencephalogram (EEG) signals. 50 subjects were used for data collection using 14 electrodes. We considered for this research 4 distraction stimuli such as Global Position Systems (GPS), music player, short message service (SMS), and mental tasks. Deriving the amplitude spectrum of three different frequency bands theta, alpha, and beta of EEG signals was based on fusion of discrete wavelet packet transform (DWPT) and FFT. Comparing the results of three different classifiers (subtractive fuzzy clustering probabilistic neural network, -nearest neighbor) was based on spectral centroid, and power spectral features extracted by different wavelets (db4, db8, sym8, and coif5). The results of this study indicate that the best average accuracy achieved by subtractive fuzzy inference system classifier is 79.21% based on power spectral density feature extracted by sym8 wavelet which gave a good class discrimination under ANOVA test.

Suggested Citation

  • Mousa Kadhim Wali & Murugappan Murugappan & Badlishah Ahmmad, 2013. "Wavelet Packet Transform Based Driver Distraction Level Classification Using EEG," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-10, November.
    • Handle: RePEc:hin:jnlmpe:297587
    DOI: 10.1155/2013/297587
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    Cited by:

    1. Yongzheng Yang & Zhigang Du & Fangtong Jiao & Fuquan Pan, 2021. "Analysis of EEG Characteristics of Drivers and Driving Safety in Undersea Tunnel," IJERPH, MDPI, vol. 18(18), pages 1-18, September.

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