Literature Review on EEG Preprocessing, Feature Extraction, and Classifications Techniques

Document Type : Original Article

Authors

1 Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Egypt

2 Computer Science and Engineering Faculty of Electronic Engineering Menofia University Egypt

3 Industrial Electronics And Control Engineering, Faculty of Electronic Engineering Menofia University Egypt

Abstract

Classification is one of the main applications of machine learning, which can group and classify the cases based on learning and development using the available data and experience knowledge. Classification is used widely in biological and medical aspects. This paper presents the problem of electroencephalogram (EEG) signal classification. Classification is the step of identifying groups or classes based on similarities between them. This step is essential to differentiate between seizure and normal periods. EEG is a monitoring tool to determine the electrical activity of the brain. The nature of EEG is quite long, so it consumes time and very difficult in processing. Epilepsy is an illness that affects people of all ages, both cases males and females. Epilepsy is a neurological disorder that makes the activities of the brain abnormal and generates seizures. Seizure symptoms vary from one people to another; it depends on the location of epileptic discharge in the cortex. To speed up the classification process and make it efficient, EEG signal needs to be preprocessed. This paper reviews the epilepsy mentality disorder and the types of seizure, preprocessing operations that performed on EEG data, a common extracted feature from the signal, and detailed view on classification techniques that can be used in this problem.

Keywords


[1]      C. Park et al., “Epileptic Seizure Detection for Multi-channel EEG with Deep Convolutional Neural Network," 2018 International Conference on Electronics, Information, and Communication (ICEIC) , pp. 1–5, 2018.
[2]      S. Lahmiri and A. Shmuel, “Accurate Classification of Seizure and Seizure-Free Intervals of Intracranial EEG Signals From Epileptic Patients,” IEEE Trans. Instrum. Meas., vol. 68, no. 3 pp. 791–796, 2019.
[3]      G. Wang, D. Ren, K. Li, D. Wang, M. Wang, and X. Yan, “EEG-based detection of epileptic seizures through the use of a directed transfer function method,” IEEE Access, vol. 6, pp. 47189–47198, 2018.
[4]      M. Fan and C. A. Chou, “Detecting Abnormal Pattern of Epileptic Seizures via Temporal Synchronization of EEG Signals,” IEEE Trans. Biomed. Eng., vol. 66, no. 3, pp. 601-608, 2018.
[5]      S. Yol, M. A. Ozdemir, A. Akan, and L. F. Chaparro, “Detection of Epileptic Seizures by the Analysis of EEG Signals Using Empirical Mode Decomposition,” 2018 Medical Technologies National Congress (TIPTEKNO), pp. 1–4, 2019.
[6]      A. Kabanov and A. Shchelkanov, “Development of a Wearable Inertial System for Motor Epileptic Seizure Detection,” 2018 14th Int. Sci. Conf. Actual Probl. Electron. Instrum. Eng. APEIE 2018 - Proc., pp. 339–342, 2018.
[7]      C. Banupriya, “A Survey on Different Techniques for Epilepsy Seizures Detection in EEG,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 6, no. 1, pp. 1970–1975, 2018.
[8]      L. Orosco, A. G. Correa, and E. Laciar, “Review: A survey of performance and techniques for automatic epilepsy detection,” J. Med. Biol. Eng., vol. 33, no. 6, pp. 526–537, 2013.
[9]      S. Sareen, S. K. Sood, and S. Kumar, “An Automatic Prediction of Epileptic Seizures Using Cloud Computing and Wireless Sensor Networks,” J. Med. Syst., 2016.
[10]   R.Vetrikani and T. Christy Bobby, “Diagnosis of epilepsy-A systematic review,” Proc. 3rd Int. Conf. Biosignals, Images Instrumentation, ICBSII, pp. 16–18, 2017.
[11]   H. Sorathiya, “A Survey on Different Methods for Epilepsy,” Int. J. Innov. Res. Sci. Eng. Technol., vol. 5, no. 2, pp. 1866–1869, 2016.
[12]   A. Yayik, E. Yildirim, Y. Kutlu, and S. Yildirim, “Epileptic State Detection: Pre-ictal, Inter-ictal, Ictal,” Int. J. Intell. Syst. Appl. Eng., vol. 3, no. 1, p. 14, 2015.
[13]   P. Plouin et al., “Epilepsy in Menkes Disease: Analysis of Clinical Stages,” Epilepsia, vol. 47, no. 2, pp. 380–386, 2006.
[14]   M. Niknazar et al., “Application of a dissimilarity index of EEG and its sub-bands on prediction of induced epileptic seizures from rat’s EEG signals,” vol. 33, no. 5, pp. 298–307, 2012.
[15]   M. Yildiz, E. Bergil, and C. Oral, “Comparison of different classification methods for the preictal stage detection in EEG signals,” Biomedical Reserch, vol. 28, no. 2, pp. 858–865, 2017.
[16]   A. Ahmadi, M. Behroozi, V. Shalchyan, and M. R. Daliri, “Phase and amplitude coupling feature extraction and recognition of Ictal EEG using VMD,” 2017 IEEE 4th Int. Conf. Knowledge-Based Eng. Innov. KBEI 2017, vol. 2018–Janua, no. December, pp. 0526–0532, 2018.
[17]   A. R. Eeg, S. Processing, and A. Human, “EEG Signal Processing for BCI Applications,” Hum. - Comput. Syst. Interact., p. 1–23, 2012.
[18]   S. Chavan, “Epileptic seizure detection using an eeg sensor,” Int. Res. J. Eng. Technol., vol. 4, no. 3, pp. 1668–1671, 2017.
[19]   V. Balasampath, J. E., K. Devarajan, J. K., and S. Bagyaraj, “EEG-Based Epilepsy Detection and Prediction,” Int. J. Eng. Technol., vol. 6, no. 3, pp. 212–216, 2014.
[20]   S. M. Akareddy and P. K. Kulkarni, “EEG signal classification for Epilepsy Seizure Detection using Improved Approximate Entropy,” Int. J. Public Heal. Sci., vol. 2, no. 1, 2014.
[21]   M. Paul, “Prediction and Detection of Epileptic Seizure by Analysing EEG Signals,” 2015.
[22]   E.Zacharaki, I.Mporas, V.Megalooikonomou, M. Koutroumanidis, V. Tsirka, and M. Richardson, “Seizure detection using EEG and ECG signals for computer-based monitoring, analysis and management of epileptic patients,” Expert Syst. Appl., vol. 42, no. 6, pp. 3227–3233, 2014.
[23]   D. Torse, V. Desai, and R. Khanai, “A Review on Seizure Detection Systems with Emphasis on Multi-domain Feature Extraction and Classification using Machine Learning,” Brain-Broad Res. Artif. Intell. Neurosci., vol. 8, no. 4, pp. 109–129, 2017.
[24]   S.  Kappel, D. Looney, D. Mandic, and P. Kidmose, “Physiological artifacts in scalp EEG and ear-EEG,” Biomed. Eng. Online, vol. 16, no. 1, p. 103, 2017.
[25]   L. Wang, W. Xue, Y. Li, M. Luo, J. Huang, and W. Cui, “Automatic Epileptic seizure detection in EEG signals Using Multi-Domain Features Extraction and Nonlinear Analysis,” pp. 1–17, 2017.
[26]   A. Savelainen, “An introduction to EEG artifacts,”, pp. 1–67, 2010.
[27]   J. A. Urigüen and B. Garcia-Zapirain, “EEG artifact removal - State-of-the-art and guidelines,” J. Neural Eng., vol. 12, no. 3, pp. 31001, 2015.
[28]   M. Iwahashi, H. Arof, P. Cumming, N. Mokhtar, and C. Y. Sai, “Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA,” IEEE J. Biomed. Heal. Informatics, vol. 22, no. 3, pp. 664–670, 2017.
[29]   V. Bono, S. Das, W. Jamal, and K. Maharatna, “Hybrid wavelet and EMD/ICA approach for artifact suppression in pervasive EEG,” J. Neurosci. Methods, vol. 267, pp. 89–107, 2016.
[30]   M. Valencia et al., “Independent Component Analysis as a Tool to Eliminate Artifacts in EEG: A Quantitative Study,” J. Clin. Neurophysiol., vol. 20, no. 4, pp. 249–257, 2003.
[31]   M. Z. Parvez and M. Paul, “Seizure Prediction using Undulated Global and Local Features,” vol. 64, no. 1, pp. 208-217, 2016.
[32]   M. Sharma, A. Dhere, R. Bilas, and U. R. Acharya, “An automatic detection of focal EEG signals using new class of time – frequency localized orthogonal wavelet filter banks,” vol. 118, pp. 217–227, 2017.
[33]   E. Bagheri, J. Jin, J. Dauwels, S. Cash, and M. B. Westover, “Fast and efficient rejection of background waveforms in interictal,” Knowledge-Based Systems, pp. 744–748, 2016.
[34]   S. Elgohary, S. Eldawlatly, and M. I. Khalil, “Epileptic seizure prediction using zero-crossings analysis of EEG wavelet detail coefficients,” in 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2016, pp. 1–6.
[35]   E. Houssein, A.  Hamad, and A.  Hassanien, “Feature Extraction of Epilepsy EEG using Discrete Wavelet Transform,” pp. 190-195, 2016.
[36]   M. Chandani, “Statistical Wavelet Features, PCA, MLPNN, SVM and K-NN Based Approach for the Classification of EEG Physiological Signal,” vol. 2, no. 5, pp. 57-65, 2017.
[37]   A. Sharmila and P. Geethanjali, “DWT Based Detection of Epileptic Seizure from EEG Signals Using Naive Bayes and k-NN Classifiers,” IEEE Access, vol. 4, pp. 7716–7727, 2016.
[38]   S. Belhadj, A. Attia, A. Bachir, A. Zoubir, and A. Ahmed, “Whole Brain Epileptic Seizure Detection using unsupervised classification,” pp. 977–982, 2016.
[39]   F. Riaz, A. Hassan, S. Rehman, I. K. Niazi, and K. Dremstrup, “EMD based Temporal and Spectral Features for the Classification of EEG Signals Using Supervised Learning,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 1, pp. 28–35, 2016.
[40]   R. B. Pachori and V. Bajaj, “Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition,” Comput. Methods Programs Biomed., vol. 104, no. 3, pp. 373–381, 2011.
[41]   F. Lotte, C. Guan, and S. Member, “Regularizing Common Spatial Patterns to Improve BCI Designs : Unified Theory and New Algorithms,” vol. 58, no. 2, pp. 355–362, 2011.
[42]   M. I. Khalid, T. Alotaiby, S. A. Aldosari, S. A. Alshebeili, M. H. Al-hameed, and F. S. Y. Almohammed, “Epileptic MEG Spikes Detection Using Common Spatial Patterns and Linear Discriminant Analysis,” IEEE Access, vol. 4, pp. 4629–4634, 2016.
[43]   A. Subasi, “Epileptic seizure detection using dynamic wavelet network,” Expert Syst. Appl., vol. 29, no. 2, pp. 343–355, 2005.
[44]   A. Gardner, H. Hall, and A. Krieger, “One-Class Novelty Detection for Seizure Analysis from Intracranial EEG,” vol. 7, pp. 1025–1044, 2006.
[45]   L. Orosco, A. G. Correa, and E. L. Leber, “Epileptic Seizures Detection Based on Empirical Mode Decomposition of EEG Signals,” Manag. epilepsy - Res. Results Treat., pp. 1–20.
[46]   N. Jrad, A. Kachenoura, I. Merlet, F. Bartolomei, A. Nica, and A. Biraben, “Automatic detection and classification of High Frequency Oscillations in depth-EEG signals,” vol. 64, no. 9,pp. 2230-2240, 2017.
[47]   S. Sengupta, D. Chanda, A. Mitra, and S. Dutta, “Computer Aided Technique for Epilepsy Detection Using Crosswavelet Transform and RBF-Kernel Based Support Vector Machine,” 2nd IEEE Int. Conf. Next Gener. Comput. Technol. (NGCT - 2016),  pp. 501–505, 2016.
[48]   S. Supriya, S. Siuly, and Y. Zhang, “Automatic epilepsy detection from EEG introducing a new edge weight method in the complex network,” Electron. Lett., vol. 52, no. 17, pp. 1430–1432, 2016.
[49]   T. Zhang and W. Chen, “LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 25, no. 8, pp. 1100–1108, 2017.
[50]   C. Chen, Z. Liu, H. Li, R. Zhou, Y. Zhang, and R. Liu, “EEG Detection Based on Wavelet Transform and SVM Method,” Proc. - 2016 IEEE Int. Conf. Smart Cloud, SmartCloud 2016, pp. 241–247, 2016.
[51]   M. Diykh, Y. Li, and P. Wen, “Classify epileptic EEG signals using weighted complex networks based community structure detection,” Expert Syst. Appl., vol. 90, pp. 87–100, 2017.
[52]   I. Mporas, V. Tsirka, E. I. Zacharaki, M. Koutroumanidis, M. Richardson, and V. Megalooikonomou, “Seizure detection using EEG and ECG signals for computer-based monitoring, analysis and management of epileptic patients,” Expert Syst. Appl., vol. 42, no. 6, pp. 3227–3233, 2015.
[53]   S. Patidar and T. Panigrahi, “Detection of epileptic seizure using Kraskov entropy applied on tunable-Q wavelet transform of EEG signals,” Biomed. Signal Process. Control, vol. 34, pp. 74–80, 2017.
[54]   M. Sharma, A. Dhere, R. B. Pachori, and U. R. Acharya, “An automatic detection of focal EEG signals using new class of time–frequency localized orthogonal wavelet filter banks,” Knowledge-Based Syst., vol. 118, pp. 217–227, 2017.
[55]   B. Yang, Y. Hu, Y. Zhu, Y. Wang, and J. Zhang, “Intracranial EEG spike detection based on rhythm information and SVM,” Proc. - 9th Int. Conf. Intell. Human-Machine Syst. Cybern. IHMSC 2017, vol. 2, pp. 382–385, 2017.
[56]   S. K. Prabhakar and H. Rajaguru, “Conceptual analysis of epilepsy classification using probabilistic mixture models,” 2017 5th Int. Winter Conf. Brain-Computer Interface, pp. 81–84, 2017.
[57]   E. Thomas, A. Temko, G. Lightbody, W. P. Marnane, and G. B. Boylan, “Gaussian mixture models for classification of neonatal seizures using EEG,” Physiol. Meas., vol. 31, no. 7, pp. 1047–1064, 2010.
[58]   H. J. Carey, M. Manic, and P. Arsenovic, “Epileptic Spike Detection with EEG using Artificial Neural Networks,” Proc. 2016 9th Int. Conf. Hum. Syst. Interact., pp. 89–95, 2016.
[59]   I. Dhif, K. Hachicha, A. Pinna, S. Hochberg, and P. Garda, “Epileptic Seizure Detection based on Expected Activity Measurement and Neural Network Classification,” pp. 2814–2817, 2017.
[60]   A. Zahra, N. Kanwal, N. ur Rehman, S. Ehsan, and K. D. McDonald-Maier, “Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition,” Comput. Biol. Med., vol. 88, pp. 132–141, 2017.
[61]   J. D. Dhande, “Daubechies Wavelet Based Neural Network Classification System for Biomedical Signal,” International Conference on Information Processing , pp. 188–191, 2015.
[62]   C. R. Azevedo, C. F. Boos, and F. M. De Azevedo, “Classification of epileptiform events in EEG signals using neural classifier based on SOM,” 2nd International Conference on Electrical Engineering and Information and Communication Technology, pp. 21–23, 2015.
[63]   L. Wang, X. Long, M. Arends, and R. Aarts, “EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures,” Journal of Neuroscience Methods, vol. 290, pp. 85–94, 2017.
[64]   Z. Mohammadpoory, M. Nasrolahzadeh, and J. Haddadnia, “Epileptic seizure detection in EEGs signals based on the weighted visibility graph entropy,” Seizure, vol. 50, pp. 202–208, 2017.
[65]   Y. Supriya, H. Wang, “Analyzing EEG Signal Data for Detection of Epileptic Seizure: Introducing Weight on Visibility Graph with Complex Network Feature,” springer, vol. 10538, pp. 56–66, 2016.
[66]   E. Tessy, and S. Manafuddin, “Time Domain Analysis of Epileptic EEG for Seizure Detection,” international conference on next generation intelligent systems, 2016.
[67]   H. Rajaguru, “Non Linear ICA and Logistic Regression for Classification of Epilepsy from EEG Signals,” Int. Conf. Electron. Commun. Aerosp. Technol., pp. 577–580, 2017.
[68]   A. Subasi and E. Erc, “Classification of EEG signals using neural network and logistic regression,” Comput. Methods Programs Biomed., vol. 78, no. 2, p. 87—99, 2005.
[69]   A. Rosenberg, J. Jin, T. Maszczyk, J. Dauwels, S. S. Cash, and M. B. Westover, “Epileptiform spike detection via convolutional neural networks,” ICASSP, pp. 754–758, 2016.
[70]   L. Vidyaratne, A. Glandon, M. Alam, and K. M. Iftekharuddin, “Deep Recurrent Neural Network for seizure detection,” pp. 1202–1207, 2016.
[71]   J. Liang, R. Lu, C. Zhang, and F. Wang, “Predicting Seizures from Electroencephalography Recordings : A Knowledge Transfer Strategy,” IEEE Conf. Healthc. Informatics, pp. 184–191, 2016.
[72]   I. Korshunova, P. Kindermans, J. Degrave, T. Verhoeven, B. H. B. Member, and J. Dambre, “Towards improved design and evaluation of epileptic seizure predictors,” vol. 9294, pp. 1–9, 2017.
[73]   V. L. Kaundanya, A. Patil, and A. Panat, “CLASSIFICATION OF EMOTIONS FROM EEG USING K-NN,” vol. 3, no. 2, pp. 103–106, 2015.
[74]   U. Awan, H. Rajput, G. Syed, R. Iqbal, and I. Sabat, “Effective Classification of EEG Signals using K-Nearest Neighbor Algorithm,” International Conference on Frontiers of Information Technology, 2016, pp. 120–124.
[75]   S. Bhattacharyya, A. Konar, D. N. Tibarewala, A. Khasnobish, and R. Janarthanan, “Performance Analysis of Ensemble Methods for Multi-class Classification of Motor Imagery EEG Signal,” pp. 712–716, 2014.
[76]   S. Wang, Y. Li, P. Wen, and G. Zhu, “Analyzing EEG Signals Using Graph Entropy Based Principle Component Analysis and J48 Decision Tree,” vol. 4, no. 1, pp. 67–72, 2016.
[77]   H. Rajaguru, “Sparse PCA and Soft Decision Tree Classifiers for Epilepsy Classification from EEG Signals,” pp. 581–584, 2017.
 
Volume 28, ICEEM2019-Special Issue
ICEEM2019-Special Issue: 1st International Conference on Electronic Eng., Faculty of Electronic Eng., Menouf, Egypt, 7-8 Dec.
2019
Pages 292-299