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Nano Biomedicine and Engineering 2019, 11(4): 391-401 https://doi.org/10.5101/nbe.v11i4.p391-401
Research Article | Open Access | Issue | Published: 17 December 2019

The Best Artificial Neural Network Parameters for Electroencephalogram Classification Based on Discrete Wavelet Transform

Show Author's Information Mousa Kadhim Wali( )
Department of Electronic Engineering, College of Technical Electrical Engineering, Middle Technical University, Baghdad, Iraq
Keywords:
Electroencephalogram (EEG), Classification, Artificial neural network (ANN), Discrete wavelet transform (DWT), Fast Fourier transform (FFT)
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Wali MK. The Best Artificial Neural Network Parameters for Electroencephalogram Classification Based on Discrete Wavelet Transform. Nano Biomedicine and Engineering, 2019, 11(4): 391-401. https://doi.org/10.5101/nbe.v11i4.p391-401
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Abstract Full text About this article

This paper presents the classification of electroencephalogram (EEG) signals using artificial neural network techniques. The signal processing of EEG signal could provide several areas for research in biomedical field. Numerous techniques can be applied to extract out the EEG characteristics in order to study and investigates the problems in the pattern recognition by its features extracted. The interesting site of signal measurement is the temporal lobe which is responsible of T3 and T4 in human electrode placement scalp. In this paper, many subjects were used to test the performance of non-neurophysiologic signals in order to investigate the electrical waves in human brain via the production of numerous EEG signals. A linear method of discrete wavelet transform (DWT) was used to gain classification with accuracy of 94.93% for testing EEG of different samples of music such as rock, jazz, classical and heavy metal using artificial neural network (ANN) with 2000 epoch, 25 nodes, 2 hidden layers. The results showed promisingly valuable EEG signal characteristics which could support the hospital staff to take care of and treat patients in the correct direction.


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About this article

The Best Artificial Neural Network Parameters for Electroencephalogram Classification Based on Discrete Wavelet Transform

Show Author's information Mousa Kadhim Wali( )
Department of Electronic Engineering, College of Technical Electrical Engineering, Middle Technical University, Baghdad, Iraq

Abstract

This paper presents the classification of electroencephalogram (EEG) signals using artificial neural network techniques. The signal processing of EEG signal could provide several areas for research in biomedical field. Numerous techniques can be applied to extract out the EEG characteristics in order to study and investigates the problems in the pattern recognition by its features extracted. The interesting site of signal measurement is the temporal lobe which is responsible of T3 and T4 in human electrode placement scalp. In this paper, many subjects were used to test the performance of non-neurophysiologic signals in order to investigate the electrical waves in human brain via the production of numerous EEG signals. A linear method of discrete wavelet transform (DWT) was used to gain classification with accuracy of 94.93% for testing EEG of different samples of music such as rock, jazz, classical and heavy metal using artificial neural network (ANN) with 2000 epoch, 25 nodes, 2 hidden layers. The results showed promisingly valuable EEG signal characteristics which could support the hospital staff to take care of and treat patients in the correct direction.

Keywords: Electroencephalogram (EEG), Classification, Artificial neural network (ANN), Discrete wavelet transform (DWT), Fast Fourier transform (FFT)

References(35)

[1]
M. Soegaard, R.F. Dam, Human computer interaction-brief intro. The encyclopedia of human-computer interaction, 2nd edition. The Intetraction Design Foundation, 2012: page number.
[2]

M.Z. Baig, N. Aslam, H.P.H. Shum, et al., Differential evolution algorithm as a tool for optimal feature subset selection in motor imagery EEG. Expert Syst. Appl. , 2017, 90: 184-195.
DOI Google Scholar
[3]
V.P. Oikonomou, K. Georgiadis, G. Liaros, et al., A comparison study on EEG signal processing techniques using motor imagery EEG data. Proceedings of the IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS). Thessaloniki, Greece, Jun. 22-24, 2017.
DOI
[4]
D. Cheng, Y, Liu, and L. Zhang, Exploring motor imagery EEG patterns for stroke patients with deep neural networks. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Calgary, AB, Canada, Apr. 15-20, 2018.
DOI
[5]
S. Kumar, R, Sharma, A, Sharma, et al., Decimation filter with common spatial pattern and fishers discriminant analysis for motor imagery classification. Proceedings of the International Joint Conference on Neural Networks (IJCNN). Vancouver, BC, Canada, Jul., 24-29, 2016.
DOI
[6]
X. Guo, X. Wu, X Gong, et al., Envelope detection based on online ICA algorithm and its application to motor imagery classification. Proceedings of the 6th International IEEE/EMBS Conference on Neural Engineering (NER). San Diego, CA, USA, Nov. 6-8, 2013.
DOI
[7]
A. Nijholt, The future of brain-computer interfacing (keynote paper). Proceedings of the 5th International Conference on Informatics, Electronics and Vision (ICIEV). Dhaka, Bangladesh, May 13-14, 2016.
DOI
[8]

J, Kevric, A. Subasi, Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system. Biomed. Signal Process. Control, 2017, 31: 398-406.
DOI Google Scholar
[9]

K. Suefusa, T. Tanaka, A comparison study of visually stimulated brain-computer and eye-tracking interfaces. J. Neural Eng., 2017, 14: 036009.
DOI Google Scholar
[10]

Y, Zhang, Y. Wang, J. Jin, et al., Sparse Bayesian learning for obtaining sparsity of EEG frequency bands based feature vectors in motor imagery classification. Int. J. Neural Syst. , 2017, 27: 1650032.
DOI Google Scholar
[11]
M.Z. Ilyas, P, Saad, M.I. Ahmad, et al., Classification of EEG signals for brain-computer interface applications: Performance comparison. Proceedings of the 6th International IEEE/EMBS Conference on Neural Engineering (NER). Ayer Keroh, Malaysia, Nov. 5-6, 2016.
DOI
[12]

D. Gajic, Z. Djurovic, S.D. Gennaro, et al., Classification of EEG signals for detection of epileptic seizures based on wavelets and statistical pattern recognition. Biomed. Eng., 2014, 26: 1450021.
DOI Google Scholar
[13]

A. Montinaro, The musical brain: Myth and science. World Neurosurgery, 2010, 73(5): 442-453.
DOI Google Scholar
[14]

R.T. Schirrmeister, J.T. Springenberg, L.D.J. Fiederer, et al., Deep learning with convolutional neural networks for EEG decoding and visualization. Hum. Brain Mapp., 2017, 38: 5391-5420.
DOI Google Scholar
[15]

Z. Tang, C. Li, S. Sun, Single-trial EEG classification of motor imagery using deep convolutional neural networks. Optik, 2017, 130: 11-18.
DOI Google Scholar
[16]

Y. Tabar, U. Halici, A novel deep learning approach for classification of EEG motor imagery signals. J. Neural Eng. , 2017, 14: 016003.
DOI Google Scholar
[17]

P. Batres-Mendoza, C. Montoro-Sanjose, E. Guerra-Hernandez, et al., Quaternion-based signal analysis for motor imagery classification from electro-encephalographic signals. Sensors, 2016, 16: 336
DOI Google Scholar
[18]

A. Liu, K. Chen, Q. Liu, et al., Feature selection for motor imagery EEG classification based on firefly algorithm and learning automata. Sensors, 2017, 17: 2576
DOI Google Scholar
[19]

Y. Wang, X. Li, H. Li, et al., Feature extraction of motor imagery electroencephalography based on time-frequency-space domains. J. Biomed. Eng. 2014, 31: 955-961.
Google Scholar
[20]

T.T. Day, Applying a locally linear embedding algorithm for feature extraction and visualization of MI-EEG. J. Sens. , 2016: 7481946.
DOI Google Scholar
[21]

R. Zazo, A. Lozano-Diez, J. Gonzalez-Dominguez, et al., Language identification in short utterances using long short-term memory (LSTM) recurrent neural networks. PLoS ONE, 2016, 11: e0146917.
DOI Google Scholar
[22]

M. Li, W. Chen, and T. Zhang, Classification of epilepsy EEG signals using DWT-based envelope analysis and neural network ensemble. Biomed. Signal Process. Control, 2017, 31: 357-365.
DOI Google Scholar
[23]
F. Lotte, A tutorial on EEG signal-processing techniques for mental-state recognition in brain computer interfaces. Guide to brain-computer music interfacing. Springer, 2014: 133-161.
DOI
[24]

F. Lotte, L. Bougrain, A. Cichocki, et al., A review of classification algorithms for EEG-based brain-computer interfaces: A 10-year update. Journal of Neural Engineering, 2018, 15(3): 55.
DOI Google Scholar
[25]
M. Wali, M. Murugappan, and B. Ahmmad, Wavelet packet transform based driver distraction level classification using EEG, Hindawi Publishing Corporation, Mathematical Problems in Engineering, 2013: Article ID 297587.
DOI
[26]

M.B. Kurt, N. Sezgin, M. Akin, et al., The ANN-based computing of drowsy level. Expert Systems with Applications, 2009, 36(2): 2534-2542.
DOI Google Scholar
[27]

M. Sifuzzaman, M. Islam, and M. Ali, Application of wavelet transform and its advantages compared to Fourier transform. Journal of Physical Sciences, 2009, 13: 121-134.
Google Scholar
[28]

M. Wali, M. Murugappan, and B. Ahmmad, Mathematical implementation of hybrid FFT and DWT for developing graphical user interface using visual basic for signal processing applications. Journal of Mechanics in Medicine and Biology, 2012, 12(5): 1240031.
DOI Google Scholar
[29]

Y. Lin, C. Wang, T. Jung, et al., EEG-based emotion recognition in music listening. IEEE Trans. on Biomedical Engineering, 2010, 57(7): 1798-1806.
DOI Google Scholar
[30]
R. Duan X. Wang, and B. Lu, EEG-based Emotion Recognition in listening music by using support vector machine and linear dynamic system. Proceedings of the 19th International Conference on Neural Information. 2012: 468-475.
DOI
[31]
N. Thammasan, K. Fukui, K. Moriyama,, et al., EEG-based emotion recognition during music listening. Proceedings of the 28th Conference of the Japanese Society of Artificial Intelligence. 2014.
[32]
P. Ghaemmaghami, N Sebe, Brain and music: Music genre classification using brain signals. Proceedings of the 24th International Conference on Signal Processing (EUSIPCO). 2016: 708-712.
DOI
[33]

H.U. Amin, W. Mumtaz, A.R. Subhani, et al., Classification of EEG signals based on pattern recognition approach. Frontiers in Computational Neuroscience, 2017.
DOI Google Scholar
[34]

D. Shon, K. Im, J. Park, et al., Emotional stress state detection using genetic algorithm-based feature selection on EEG signals. Int. J. Environ. Res. Public Health, 2018, 15: 2461.
DOI Google Scholar
[35]

M. Zulkifley, S. Abdani, EEG signals classification by using convolutional neural networks. SASSP, 2019.
Google Scholar
Publication history
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Publication history

Received: 01 September 2019
Accepted: 16 December 2019
Published: 17 December 2019
Issue date: December 2019

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© Mousa Kadhim Wali.

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This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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