Call for Paper - August 2022 Edition
IJCA solicits original research papers for the August 2022 Edition. Last date of manuscript submission is July 20, 2022. Read More

The Classificaton of EEG Signals Recorded in Drunk and Non-Drunk People

Print
PDF
International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 68 - Number 10
Year of Publication: 2013
Authors:
Ziya Ek?i
Akif Akgül
Mehmet Recep Bozkurt
10.5120/11619-7018

Ziya Ekhi, Akif Akgul and Mehmet Recep Bozkurt. Article: The Classificaton of EEG Signals Recorded in Drunk and Non-Drunk People. International Journal of Computer Applications 68(10):40-44, April 2013. Full text available. BibTeX

@article{key:article,
	author = {Ziya Ekhi and Akif Akgul and Mehmet Recep Bozkurt},
	title = {Article: The Classificaton of EEG Signals Recorded in Drunk and Non-Drunk People},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {68},
	number = {10},
	pages = {40-44},
	month = {April},
	note = {Full text available}
}

Abstract

Alcoholic beverages are widely used in many societies. People can maintain their everyday lives while using a certain amount of alcohol. However, when alcohol is used intensively, it prevents healthy thinking, undermines decision making and reflex functions. This decrease in reflexes brings increase in traffic and job accidents. In order to determine the capability of a person to do a job, the detection of alcohol level is important. Nowadays, breathalyzers are used for the purpose of such detection. These devices measure the amount of alcohol rather than loss of function caused by alcohol. But the amount of alcohol taken show different effects from person to person. In this study people were tested and determined whether they were alcoholic with the help of EEG data. Preprocessing was performed on the EEG data set before the process of detection, followed by the training of ANN (Artificial Neural Network) and its test. Based on the obtained best performance value, an interface was designed on MATLAB for users.

References

  • Figure of measuring EEG signals, http://bmm. etu. edu. tr/tr/content/biyoelektronik-sinyal-isleme, Last accessed 20. 03. 2012.
  • Malar E. , Gauthaam M. , Chakravarthy D. , 2011 , "A Novel Approach for the Detection of Drunken Driving using the Power Spectral Density Analysis of EEG", International Journal of Computer Applications (0975 – 8887) , Volume 21– No. 7, pp. 10-14.
  • Pradhan N. , Sadasivan P. K, Arunodaya G. R. , 1996, "Detection of Seizure Activity in EEG by An Artificial Neural Network: A Preliminary Study", Computers and Biomedical Research, No. 29, pp. 303–313.
  • Lin R. , Lee R. , Tseng C. , Zhou H. , Chao C. , Jiang J. , "A New Approach for Identifying Sleep Apnea Syndrome using Wavelet Transform and Neural Networks", Biomedical Engineering: Applications, Basis and Communications, 2006, Vol. 18, pp. 138 -143.
  • Hazarika N. , Chen J. Z. , Tsoi A. C. , Sergejew A. , 1997, "Classification of EEG Signals Using the Wavelet Transform", Signal Processing, Vol. 59, No. 1, pp. 61–72.
  • K?ym?k M. K. , Ak?n M. , Subas? A. , 2004, "Automatic Recognition of Alertness Level by Using Wavelet Transform and Artificial Neural Network", Journal of – euroscience Methods, vol. 139, pp. 231–240.
  • Bellotti R. , De Carlo F. , Tommaso M. D, Lucente M. , 2007, "Classification of Spontaneous EEG Signals in Migraine", Physica A: Statistical Mechanics and its Applications, vol. 382, Issue 2, pp. 549-556.
  • Puthankattil S. D. , Joseph P. K. , "Classification of EEG Signals in Normal and Depression Conditions by ANN Using RWE and Signal Entropy", Journal of Mechanics in Medicine and Biology, 2012, Vol. 12, No. 4, pp. 1-13.
  • Sabeti M. , Katebi S. D. , Boostani R. , Price G. W. , 2011, "A New Approach for EEG Signal Classification of Schizophrenic and Control Participants", Expert Systems with Applications, vol. 38, pp. 2063-2071.
  • Olejarczyk E. , Sobieszek A. , Runder R. , Marciniak R. , Wartak M. , Stasiowski M. , Jalowiecki P. , 2010, "Spectral analysis of the EEG-signal Registered during Anaesthesia Induced by Propofol and Maintained by Fluorinated Inhalation Anaesthetics", Biocybernetics and Biomedical Engineering, vol. 30, pp. 55-70.
  • ?ahin C. , Ogulata S. N. , Aslan K. , Bozdemir H. , Erol R. , "A Neural Network-Based Classification Model for Partial Epilesy by EEG Signals", International Journal of Pattern Recognition and Artificial Intelligence, 2008, Vol. 22, No. 5, pp. 973-985.
  • Batar H. , 2007, "Analysis Of EEG Signals Using The Wavelet Transform And Artificial Neural Network", M. Sc. Thesis, Süleyman Demirel University Graduate School of Applied and Natural Sciences, Isparta, Turkey.
  • Yazgan E. , Korürek M. , 1995. "T?p Elektroni?i", ?stanbul, Turkey.
  • Tyvaert L. ,Van P. L. , Grova C. , Dubeau F. , Gotman J. , 2008, "Effects of Fluctuating Physiological Rhythms During Prolonged EEG-fMRI Studies" Clinical Neurophysiology 119, pp. 2762–2774.
  • Bozkurt M. R. , 2007, "Pre-Processing And Classification Of EMG Signals By Using Modern Methods", PhD Thesis, Sakarya University Graduate School of Applied and Natural Sciences, Sakarya, Turkey.
  • Vatansever F. , Do?al? G. , 2011, "Klasik Enterpolasyon Yöntemleri ve Yapay Sinir A?? Yakla??mlar?n?n Kar??la?t?r?lmas?", 6th International Advanced Technologies Symposium (IATS'11), pp. 51-54.
  • Elmas Ç. , 2003, Yapay Sinir A?lar?, Seçkin Yay?nc?l?k, Ankara.
  • Hayes M. H, 1996 , "Statistical Digital Signal Processing and Modelling", New York: John Wiley and Sons.
  • http://archive. ics. uci. edu/ml/databases/eeg/eeg_full/