An EEG Based Vehicle Driving Safety System Using Automotive CAN Protocol

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2015 by IJETT Journal
Volume-26 Number-4
Year of Publication : 2015
Authors : Renji V.Mathew, Jasmin Basheer
  10.14445/22315381/IJETT-V26P236

MLA 

Renji V.Mathew, Jasmin Basheer"An EEG Based Vehicle Driving Safety System Using Automotive CAN Protocol", International Journal of Engineering Trends and Technology (IJETT), V26(4),212-216 August 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
A real-time drowsiness detection system in vehicles using single channel EEG dry sensor is described. Drowsy driving is a severe issue that leads to traffic accidents. Sleeping can be identified by the physical activities such as eye blink level, yawning, gripping force on the wheel etc. In some cases people will mentally sleep with eyes open for a short time, will leads to accidents. In this system, analysing the mental activities of human brain using EEG (Electroencephalography) based on BCI (Brain Computer Interface). Human brain consists of millions of interconnected neurons. According to the human activities unique electric brain signal will form. Drowsiness is detected by analysing the power of EEG bands. An algorithm is developed for this detection. In this work, using single dry electrode brain wave sensor (Neurosky - Mindwave) which can collect EEG based brain signals of different frequency and amplitude and it will convert these signals into packets and transmit wirelessly to next section for checking the attention level, drowsiness detection and gives the drowsy driving alert and keeps the vehicle to be in self-controlled function until awakened state. This can save a lot of lives in road transportation. The nodes in this system are implemented on ARM7 (Advanced RISC Machine) cores (LPC 2148). Communications between these nodes are accomplished through automotive CAN (Controller Area Network) protocol.

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Keywords
ARM, BCI, Drowsiness Detection, CAN.