IJBTT

An EEG Based Vehicle Driving Safety System Using Automotive CAN Protocol

	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
	DOI :  10.14445/22315381/IJETT-V26P236

Citation 

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.

 References

[1] Chin Teng Lin, Chun Hsiang Chuang, Chih Sheng Huang, Shu Fang Tsai, Shao Wei Lu, Yen Hsuan Chen, and Li Wei Ko, ?Wireless and Wearable EEG System for Evaluating Driver Vigilance IEEE Transactions On Biomedical Circuits And Systems, Vol. 8, No. 2, April 2014.
[2] F.C. Lin, L.W. Ko, C.H. Chuang, T.P. Su, and C.T. Lin, ?Generalized EEG based drowsiness prediction system by using a self-organizing neural fuzzy system, IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 59, no. 9, pp. 2044–2055, Sep. 2012.
[3] A. J. Smola and B. Scholkopf, ?A tutorial on support vector regression, Statist. Comput, vol. 14, no. 3, pp. 199–222, 2004.
[4] W. Karlen, C. Mattiussi, and D. Floreano, ?Sleep and wake classification with ECG and respiratory effort signals IEEE Trans. Biomed. Circuits Syst., vol. 3, no. 2, pp. 71–78, Apr. 2009.
[5] Bryan Van Hal, Samhita Rhodes, Bruce Dunne and Robert Bossemeyer ?Low-Cost EEG-based Sleep Detection Engineering in Medicine and Biology Society (EMBC), 36th Annual International Conference of the IEEE 4571 – 4574 August 2014.
[6] AlZubi H.S., Al-Nuaimy W., Al-Zubi, N.S. ?EEG-based Driver Fatigue Detection Developments in e-systems Engineering (DeSE), Sixth International Conference, pp.111- 114, IEEE 2013.
[7] Farsi. M, Ratcliff. K, Barbosa M. “An overview of controller area network Computing & Control Engineering Journal Volume: 10, Issue: 3 pp.113 – 120, IET Journals & Magazines 1999.
[8] Fredriksson, Kvaser AB, ?CAN for critical embedded automotive networks Micro, IEEE Volume: 22, Issue: 4, pp.28–35, IEEE Journals & Magazines 2002.
[9] Abu Asaduzzaman, Sandip Bhowmick, Md Moniruzzaman ?Design and evaluation of controller area network for automotive applications American Journal of Embedded Systems and Applications Vol. 2,pp. 29-37, 2014.
[10] Dajeong Kim, Hyungseob Han, Sangjin Cho and Uipil Chong ?Detection of Drowsiness with eyes open using EEG Based Power Spectrum Analysis Strategic Technology (IFOST), 7th International Forum, Sept 2012.
[11] Rekha Saini, Vandna Saini ?Driver Drowsiness Detection System and Techniques: A Review International Journal of Computer Science and Information Technologies, Vol. 5, no 3, pp.4245-4249, 2014.
[12] CAN in Automation (CiA) [Online]. Available: http://www.can-cia.org/
[13] ?LPC2148 datasheet, NXP Semiconductors, Tokyo Japan.
[14] ?MCP2510 Stand-Alone CAN Controller with SPI datasheet Microchip Technology Inc, Arizona, USA.
[15] ?MCP2551 - Interface- Controller Area Network (CAN) datasheet Microchip Technology Inc, Arizona, USA.
[16] The Neurosky Website [Online]. Available: http://neurosky.com/biosensors/eeg-sensor/

Keywords
ARM, BCI, Drowsiness Detection, CAN.