Skip to main content
SpringerLink
Log in

Survey for Electroencephalography EEG Signal Classification Approaches

  • Conference paper
  • First Online:
Mobile Computing and Sustainable Informatics

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 68))

Abstract

This paper presents a literature survey for electroencephalogram (EEG) signal classification approaches based on machine learning algorithms. EEG classification plays a vital role in many health applications using machine learning algorithms. Mainly, they group and classify patient signals based on learning and developing specific features and metrics. In this paper, 32 highly reputed research publications are presented focusing on the designed and implemented approach, applied dataset, their obtained results and applied evaluation. Furthermore, a critical analysis and statement are provided for the surveyed papers and an overall analysis in order to have all the papers under an evaluation comparison. SVM, ANN, KNN, CNN, LDA, multi-classifier and more other classification approaches are analyzed and investigated. All classification approaches have shown potential accuracy in classifying EEG signals. Evidently, ANN has shown higher persistency and performance than all other models with 97.6% average accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. A. Sharmila, P. Geethanjali, DWT based detection of epileptic seizure from EEG signals using Naive Bayes and k-NN classifiers. IEEE Access 4(c), 7716–7727 (2016). https://doi.org/10.1109/ACCESS.2016.2585661

  2. K. Venkatachalam, A. Devipriya, J. Maniraj, M. Sivaram, A. Ambikapathy, S.A. Iraj, A novel method of motor imagery classification using EEG signal. Artif. Intell. Med. 103, 101787 (2020). https://doi.org/10.1016/j.artmed.2019.101787

  3. M. Diykh, Y. Li, P. Wen, EEG sleep stages classification based on time domain features and structural graph similarity. IEEE Trans. Neural Syst. Rehabil. Eng. 24(11), 1159–1168 (2016). https://doi.org/10.1109/TNSRE.2016.2552539

    Article  Google Scholar 

  4. Z. Mousavi, T. Yousefi Rezaii, S. Sheykhivand, A. Farzamnia, S.N. Razavi, Deep convolutional neural network for classification of sleep stages from single-channel EEG signals. J. Neurosci. Methods 324 (2019). https://doi.org/10.1016/j.jneumeth.2019.108312

  5. Y. Luo et al., EEG-based emotion classification using spiking neural networks. IEEE Access 8, 46007–46016 (2020). https://doi.org/10.1109/ACCESS.2020.2978163

    Article  Google Scholar 

  6. K.K. Al-Nassrawy, D. Al-Shammary, A.K. Idrees, High performance fractal compression for EEG health network traffic. Procedia Comput. Sci. 167, 1240–1249 (2020). ISSN 1877-0509

    Google Scholar 

  7. S. Lahmiri, A. Shmuel, Accurate classification of seizure and seizure-free intervals of intracranial EEG signals from epileptic patients. IEEE Trans. Instrum. Meas. 68(3), 791–796 (2019). https://doi.org/10.1109/TIM.2018.2855518

    Article  Google Scholar 

  8. Z. Li, M. Shen, Classification of mental task EEG signals using wavelet packet entropy and SVM, in 2007 8th International Conference on Electronic Measurement and Instruments, ICEMI, 2007, pp. 3906–3909. https://doi.org/10.1109/ICEMI.2007.4351064

  9. L. Shen, X. Dong, Y. Li, Analysis and classification of hybrid EEG features based on the depth DRDS videos. J. Neurosci. Methods 338, 108690 (2020). https://doi.org/10.1016/j.jneumeth.2020.108690

  10. Y. Li, X.D. Wang, M.L. Luo, K. Li, X.F. Yang, Q. Guo, Epileptic seizure classification of EEGs using time-frequency analysis based multiscale radial basis functions. IEEE J. Biomed. Health Inform. 22(2), 386–397 (2018). https://doi.org/10.1109/JBHI.2017.2654479

    Article  Google Scholar 

  11. Z. Chen, G. Lu, Z. Xie, W. Shang, A unified framework and method for EEG-based early epileptic seizure detection and epilepsy diagnosis. IEEE Access 8, 20080–20092 (2020). https://doi.org/10.1109/ACCESS.2020.2969055

    Article  Google Scholar 

  12. L. Chisci et al., Real-time epileptic seizure prediction using AR models and support vector machines. IEEE Trans. Biomed. Eng. 57(5), 1124–1132 (2010). https://doi.org/10.1109/TBME.2009.2038990

    Article  Google Scholar 

  13. S.T. George, M.S.P. Subathra, N.J. Sairamya, L. Susmitha, M. Joel Premkumar, Classification of epileptic EEG signals using PSO based artificial neural network and tunable-Q wavelet transform. Biocybern. Biomed. Eng. 40(2), 709–728 (2020). https://doi.org/10.1016/j.bbe.2020.02.001

  14. K. Samiee, P. Kovács, M. Gabbouj, Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform. IEEE Trans. Biomed. Eng. 62(2), 541–552 (2015). https://doi.org/10.1109/TBME.2014.2360101

    Article  Google Scholar 

  15. L. Guo, D. Rivero, J. Dorado, J.R. Rabuñal, A. Pazos, Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. J. Neurosci. Methods 191(1), 101–109 (2010). https://doi.org/10.1016/j.jneumeth.2010.05.020

    Article  Google Scholar 

  16. S.K. Satapathy, S. Dehuri, A.K. Jagadev, EEG signal classification using PSO trained RBF neural network for epilepsy identification. Inform. Med. Unlocked 6, 1–11 (2017). https://doi.org/10.1016/j.imu.2016.12.001

  17. M.H. Bhatti et al., Soft computing-based EEG classification by optimal feature selection and neural networks. IEEE Trans. Ind. Inf. 15(10), 5747–5754 (2019). https://doi.org/10.1109/TII.2019.2925624

    Article  Google Scholar 

  18. S. Raghu, N. Sriraam, Y. Temel, S.V. Rao, P.L. Kubben, EEG based multi-class seizure type classification using convolutional neural network and transfer learning. Neural Netw. 124, 202–212 (2020). https://doi.org/10.1016/j.neunet.2020.01.017

    Article  Google Scholar 

  19. S. Ramakrishnan, A.S. Muthanantha Murugavel, P. Saravanan, Epileptic EEG signal classification using multi-class convolutional neural network, in Proceedings of International Conference on Vision towards Emerging Trends in Communication and Networking, ViTECoN 2019, 2019, pp. 1–5. https://doi.org/10.1109/ViTECoN.2019.8899453

  20. J. Lian, Y. Zhang, R. Luo, G. Han, W. Jia, C. Li, Pair-wise matching of EEG signals for epileptic identification via convolutional neural network. IEEE Access 8, 40008–40017 (2020). https://doi.org/10.1109/ACCESS.2020.2976751

    Article  Google Scholar 

  21. U.I. Awan, U.H. Rajput, G. Syed, R. Iqbal, I. Sabat, M. Mansoor, Effective classification of EEG signals using K-nearest neighbor algorithm, in Proceedings of 14th International Conference on Frontiers of Information Technology FIT 2016, 2017, pp. 120–124. https://doi.org/10.1109/FIT.2016.030

  22. J.T. Oliva, J.L.G. Rosa, Classification for EEG report generation and epilepsy detection. Neurocomputing 335, 81–95 (2019). https://doi.org/10.1016/j.neucom.2019.01.053

    Article  Google Scholar 

  23. Y. You, W. Chen, T. Zhang, Motor imagery EEG classification based on flexible analytic wavelet transform. Biomed. Signal Process. Control 62, 102069 (2020). https://doi.org/10.1016/j.bspc.2020.102069

    Article  Google Scholar 

  24. W.Y. Hsu, EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features. J. Neurosci. Methods 189(2), 295–302 (2010). https://doi.org/10.1016/j.jneumeth.2010.03.030

    Article  Google Scholar 

  25. J. das C. Rodrigues, P.P.R. Filho, E. Peixoto, N. Arun Kumar, V.H.C. de Albuquerque, Classification of EEG signals to detect alcoholism using machine learning techniques. Pattern Recognit. Lett. 125, 140–149 (2019). https://doi.org/10.1016/j.patrec.2019.04.019

  26. A. Piryatinska, B. Darkhovsky, A. Kaplan, Binary classification of multichannel-EEG records based on the ϵ-complexity of continuous vector functions. Comput. Methods Programs Biomed. 152, 131–139 (2017). https://doi.org/10.1016/j.cmpb.2017.09.001

    Article  Google Scholar 

  27. X. Hu, S. Yuan, F. Xu, Y. Leng, K. Yuan, Q. Yuan, Scalp EEG classification using deep Bi-LSTM network for seizure detection. Comput. Biol. Med. 124, 103919 (2020). https://doi.org/10.1016/j.compbiomed.2020.103919

    Article  Google Scholar 

  28. R. San-Segundo, M. Gil-Martín, L.F. D’Haro-Enríquez, J.M. Pardo, Classification of epileptic EEG recordings using signal transforms and convolutional neural networks. Comput. Biol. Med. 109, 148–158 (2019). https://doi.org/10.1016/j.compbiomed.2019.04.031

    Article  Google Scholar 

  29. M. Dobiáš, J. St’Astny, Movement EEG classification using parallel hidden Markov models, in International Conference on Applied Electronics, Sept 2016, pp. 65–68. https://doi.org/10.1109/AE.2016.7577243

  30. A.V. Misiukas Misiūnas, T. Meškauskas, R. Samaitienė, Algorithm for automatic EEG classification according to the epilepsy type: Benign focal childhood epilepsy and structural focal epilepsy. Biomed. Signal Process. Control 48, 118–127 (2019). https://doi.org/10.1016/j.bspc.2018.10.006

  31. A.B. Peachap, D. Tchiotsop, Epileptic seizures detection based on some new Laguerre polynomial wavelets, artificial neural networks and support vector machines. Inform. Med. Unlocked 16, 100209 (2019). https://doi.org/10.1016/j.imu.2019.100209

  32. S. Raghu, N. Sriraam, A.S. Hegde, P.L. Kubben, A novel approach for classification of epileptic seizures using matrix determinant. Expert Syst. Appl. 127, 323–341 (2019). https://doi.org/10.1016/j.eswa.2019.03.021

    Article  Google Scholar 

  33. M. Savadkoohi, T. Oladduni, A machine learning approach to epileptic seizure prediction using electroencephalogram (EEG) signal. Biocybern. Biomed. Eng. 1–14 (2020). https://doi.org/10.1016/j.bbe.2020.07.004

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Information Technology, University of Al-Qadisiyah, Dewaniyah, Iraq

    Safaa S. Al-Fraiji & Dhiah Al-Shammary

Authors
  1. Safaa S. Al-Fraiji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dhiah Al-Shammary
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dhiah Al-Shammary .

Editor information

Editors and Affiliations

  1. Institute of Engineering, Tribhuvan University, Kirtipur, Nepal

    Subarna Shakya

  2. Czech Technical University in Prague, Praha, Czech Republic

    Robert Bestak

  3. Gerald Schwartz School of Business, St. Francis Xavier University, Antigonish, NS, Canada

    Ram Palanisamy

  4. Department of Computer Science, Texas Southern University, Houston, TX, USA

    Khaled A. Kamel

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Al-Fraiji, S.S., Al-Shammary, D. (2022). Survey for Electroencephalography EEG Signal Classification Approaches. In: Shakya, S., Bestak, R., Palanisamy, R., Kamel, K.A. (eds) Mobile Computing and Sustainable Informatics. Lecture Notes on Data Engineering and Communications Technologies, vol 68. Springer, Singapore. https://doi.org/10.1007/978-981-16-1866-6_14

Download citation

Publish with us

Policies and ethics

Search

Navigation