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EEG Based Oscitancy Classification System for Accidental Prevention

  • Jay Sarraf
  • Satarupa Chakrabarty
  • Prasant Kumar PattnaikEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 516)

Abstract

Drowsiness and alcohol consumption has always been the root cause of the road mishaps that takes place. Excessive consumption of alcohol gives rises to many complications such as it prevents healthy thinking and slows down reflex actions. So in order to determine a person’s capability to do a job, his oscitancy tracking is very much important. In this paper, we classify the EEG signal taken from 50 drunk and 50 non drunk people. Various band decomposition of the data was done using the DWT (Discrete Wavelet transformation) and further trained by ANN (Artificial Neural Network) approach. Further we suggest an intelligence system which monitors and decide whether the driver should be allowed to drive the vehicle or not based on his drowsiness classification which can prevent accidents with drunken drivers.

Keywords

EEG ANN Drunk Alcohol DWT 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Jay Sarraf
    • 1
  • Satarupa Chakrabarty
    • 1
  • Prasant Kumar Pattnaik
    • 1
    Email author
  1. 1.School of Computer EngineeringKIIT UniversityBhubaneswarIndia

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