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Exploring the Performance of EEG Signal Classifiers for Alcoholism

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Advances in Artificial Intelligence and Data Engineering (AIDE 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1133))

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Abstract

Alcoholism is a tendency to continually rely on alcohol. Unchecked ingestion leads to gradual deteriorating mental health of the abusers. To study the changes in brain activity, electroencephalography (EEG) is one of the acute and low-cost methods. In this study, different sampling rates are experimented on the input EEG signals. The most favorable sampling rate is applied to extract features using the statistical parameters such as mean, median, variance and standard deviation. The best result obtained is an accuracy of 96.84% with support vector machine (SVM) classifier for a sampling rate of 16 by combining all the features extracted using aforementioned statistical parameters.

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Abbreviations

ANN:

Artificial Neural Networks

BCI:

Brain-Computer Interface

EEG:

Electroencephalography

KNN:

K-Nearest Neighbors

RAM:

Random-Access Memory

RBF:

Radial Basis Function

SVM:

Support Vector Machine

SWT:

Stationary Wavelet Transform

References

  1. Adeli H, Zhou Z, Dadmehr N (2003) Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods 123(1):69–87. https://doi.org/10.1016/S0165-0270(02)00340-0

    Article  Google Scholar 

  2. Ahmadi A, Shalchyan V, Mohammad RD (2017) A new method for epileptic seizure classification in EEG using adapted wavelet packets. In: 2017 electric electronics, computer science, biomedical engineerings’ meeting (EBBT). IEEE. https://doi.org/10.1109/EBBT.2017.7956756

  3. Bablani A, Edla DR, Dodia S (2018) Classification of EEG data using k-nearest neighbor approach for concealed information test. Procedia Comput Sci 143:242–249. https://doi.org/10.1016/j.procs.2018.10.392

    Article  Google Scholar 

  4. Bayram K, Ayyuce Sercan M, Kizrak Bolat B (2013) Classification of EEG signals by using support vector machines. In: 2013 IEEE INISTA. IEEE. https://doi.org/10.1109/INISTA.2013.6577636

  5. Bhuvaneswari P, Satheesh Kumar J (2013) Support vector machine technique for EEG signals. Int J Comput Appl 63(13)

    Google Scholar 

  6. Cakmak R, Zeki AM (2015) Neuro signal based lie detection. In: 2015 IEEE international symposium on robotics and intelligent sensors (IRIS). IEEE. https://doi.org/10.1109/IRIS.2015.7451606

  7. Chan H-T et al (2017) Applying EEG in criminal identification research. In: 2017 international conference on applied system innovation (ICASI). IEEE. https://doi.org/10.1109/ICASI.2017.7988484

  8. Gandhi T et al (2010) Expert model for detection of epileptic activity in EEG signature. Expert Syst Appl 37(4):3513–3520. https://doi.org/10.1016/j.eswa.2009.10.036

    Article  Google Scholar 

  9. Ghosh-Dastidar S, Adeli H, Dadmehr N (2008) Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection. IEEE Trans Biomed Eng 55(2):512–518. https://doi.org/10.1109/TBME.2007.905490

    Article  Google Scholar 

  10. Guler I, Ubeyli ED (2007) Multiclass support vector machines for EEG-signals classification. IEEE Trans Inf Technol Biomed 11(2):117–126. https://doi.org/10.1109/TITB.2006.879600

    Article  Google Scholar 

  11. Guler NF, Ubeyli ED, Guler I (2005) Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst Appl 29(3):506–514

    Article  Google Scholar 

  12. Hanouneh S et al (2015) Functional connectivity of EEG regional delta and inter-regional gamma activity correlates with memory recall. In: 2015 IEEE international conference on control system, computing and engineering (ICCSCE). IEEE. https://doi.org/10.1109/ICCSCE.2015.7482237

  13. Harikumar R, Sunil Kumar P (2015) Dimensionality reduction techniques for processing epileptic encephalographic signals. Biomed Pharmacol J 8(1):103–106. https://doi.org/10.13005/bpj/587

    Article  Google Scholar 

  14. Huang J et al (2018) An improved kNN based on class contribution and feature weighting. In: 2018 10th international conference on measuring technology and mechatronics automation (ICMTMA). IEEE. https://doi.org/10.1109/ICMTMA.2018.00083

  15. Kaundanya VL, Patil A, Panat A (2015) Performance of k-NN classifier for emotion detection using EEG signals. In: 2015 international conference on communications and signal processing (ICCSP). IEEE. https://doi.org/10.1109/ICCSP.2015.7322687

  16. Kirmizi-Alsan E et al (2006) Comparative analysis of event-related potentials during Go/NoGo and CPT: decomposition of electrophysiological markers of response inhibition and sustained attention. Brain Res 1104(1):114–128. https://doi.org/10.1016/j.brainres.2006.03.010

    Article  Google Scholar 

  17. Murugesan M, Sukanesh R (2009) Towards detection of brain tumor in electroencephalogram signals using support vector machines. Int J Comput Theory Eng 1(5):622

    Article  Google Scholar 

  18. Rachman NT, Tjandrasa H, Fatichah C (2016) Alcoholism classification based on EEG data using independent component analysis (ICA), wavelet de-noising and probabilistic neural network (PNN). In: 2016 international seminar on intelligent technology and its applications (ISITIA). IEEE. https://doi.org/10.1109/ISITIA.2016.7828626

  19. Rout N (2014) Analysis and classification technique based on ANN for EEG signals. IJCSIT 5(4):5103–5105

    Google Scholar 

  20. Shahid A et al (2013) Epileptic seizure detection using the singular values of EEG signals. In: 2013 ICME international conference on complex medical engineering. IEEE. https://doi.org/10.1109/ICCME.2013.6548330

  21. Siuly S, Li Y, Zhang Y (2016) Significance of EEG signals in medical and health research. EEG signal analysis and classification. Springer, Cham, pp 23–41. https://doi.org/10.1007/978-3-319-47653-7_2

  22. Srinivasan V, Eswaran C, Sriraam N (2007) Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans Inf Technol Biomed 11(3):288–295. https://doi.org/10.1109/TITB.2006.884369

    Article  Google Scholar 

  23. Subasi A, Ismail Gursoy M (2010) EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl 37(12):8659–8666. https://doi.org/10.1016/j.eswa.2010.06.065

    Article  Google Scholar 

  24. State University of New York Health Center, Neurodynamics Laboratory (1999) UCI machine learning repository, 13 October 1999 (online). Available at: https://archive.ics.uci.edu/ml/datasets/EEG+Database

  25. Supriya S et al (2016) Weighted visibility graph with complex network features in the detection of epilepsy. IEEE Access 4:6554–6566. https://doi.org/10.1109/ACCESS.2016.2612242

    Article  Google Scholar 

  26. Thiyagarajan M (2019) Brain tumour detection via EEG signals. Indian J Appl Res 9:213–215

    Google Scholar 

  27. Yasmeen S, Karki MV (2017) Neural network classification of EEG signal for the detection of seizure. In: 2017 2nd IEEE international conference on recent trends in electronics, information & communication technology (RTEICT). IEEE. https://doi.org/10.1109/RTEICT.2017.8256658

  28. Yeo MVM et al (2009) Can SVM be used for automatic EEG detection of drowsiness during car driving? Saf Sci 47(1):115–124. https://doi.org/10.1016/j.ssci.2008.01.007

    Article  Google Scholar 

  29. Zavar M et al (2011) Evolutionary model selection in a wavelet-based support vector machine for automated seizure detection. Expert Syst Appl 38(9):10751–10758. https://doi.org/10.1016/j.eswa.2011.01.087

    Article  Google Scholar 

  30. Zukov I, Ptacek R, Fischer S (2008) EEG abnormalities in different types of criminal behavior. Activitas Nervosa Superior 50(4):110–113. https://doi.org/10.1007/BF03379552

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Sahyadri College of Engineering and Management, Mangaluru, Karnataka, India

    Nishitha Lakshmi, Rani Adhaduk, Nidarsh Nithyananda, S. Rashwin Nonda & K. Pushpalatha

Authors
  1. Nishitha Lakshmi
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  2. Rani Adhaduk
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  3. Nidarsh Nithyananda
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  4. S. Rashwin Nonda
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  5. K. Pushpalatha
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Corresponding author

Correspondence to Nishitha Lakshmi .

Editor information

Editors and Affiliations

  1. NMAM Institute of Technology, Udupi, India

    Niranjan N. Chiplunkar

  2. Ritsumeikan University, Shiga, Japan

    Takanori Fukao

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Lakshmi, N., Adhaduk, R., Nithyananda, N., Rashwin Nonda, S., Pushpalatha, K. (2021). Exploring the Performance of EEG Signal Classifiers for Alcoholism. In: Chiplunkar, N.N., Fukao, T. (eds) Advances in Artificial Intelligence and Data Engineering. AIDE 2019. Advances in Intelligent Systems and Computing, vol 1133. Springer, Singapore. https://doi.org/10.1007/978-981-15-3514-7_12

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