The State of the Art in Feature Extraction Methods for EEG Classification
DOI:
https://doi.org/10.21928/uhdjst.v3n2y2019.pp16-23Keywords:
Electroencephalogram (EEG); Epileptic seizure detection; feature extraction; time-frequency analysis; classification.Abstract
Epileptic seizure is a neurological disease that is common around the world and there are many types (e.g. Focal aware seizures and atonic seizure) that are caused by synchronous or abnormal neuronal activity in the brain. A number of techniques are available to detect the brain activities that lead to Epileptic seizures; one of the most common one is Electroencephalogram (EEG) that uses visual scanning to measure brain activities generated by nerve cells in the cerebral cortex. The techniques make use of different features detected by EEG to decide on the occurrence and type of seizures. In this paper we review EEG features proposed by different researches for the purpose of Epileptic seizure detection, also analyze, and compare the performance of the proposed features.
References
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[20] L. Boubchir, S. Al-Maadeed, and A. Bouridane, “Haralick feature extraction from time-frequency images for epileptic seizure detection and classification of EEG data,” in 2014 26th International Conference on Microelectronics (ICM), 2014, pp. 32–35.
[21] N. Kannathal, M. L. Choo, U. R. Acharya, and P. K. Sadasivan, “Entropies for detection of epilepsy in EEG,” Computer methods and programs in biomedicine, vol. 80, no. 3, pp. 187–94, 2005.
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[23] A. SUBASI, “EEG signal classification using wavelet feature extraction and a mixture of expert model,” Expert Systems with Applications, vol. 32, no. 4, pp. 1084–1093, 2007.
[2] R. S. Fisher, C. Acevedo, A. Arzimanoglou, A. Bogacz, J. H. Cross, C. E. Elger, J. Engel, L. Forsgren, J. A. French, M. Glynn, D. C. Hesdorffer, B. I. Lee, G. W. Mathern, S. L. Moshé, E. Perucca, I. E. Scheffer, T. Tomson, M. Watanabe, and S. Wiebe, “ILAE official report: a practical clinical definition of epilepsy.,” Epilepsia, vol. 55, no. 4, pp. 475–82, 2014.
[3] B. Abou-Khalil and K. E. Misulis, Atlas of EEG \& seizure semiology: Text with DVD. Butterworth-Heinemann, 2005.
[4] H. R. Mohseni, A. Maghsoudi, and M. B. Shamsollahi, “Seizure detection in EEG signals: a comparison of different approaches.,” Conf Proc IEEE Eng Med Biol Soc, vol. Suppl, pp. 6724–7, 2006.
[5] B. Boashash, L. Boubchir, and G. Azemi, “A methodology for time-frequency image processing applied to the classification of non-stationary multichannel signals using instantaneous frequency descriptors with application to newborn EEG signals,” EURASIP Journal on Advances in Signal Processing, vol. 2012, no. 1, p. 117, 2012.
[6] L. Boubchir, S. Al-Maadeed, A. Bouridane, and A. A. Chérif, “Time-frequency image descriptors-based features for EEG epileptic seizure activities detection and classification,” in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015, pp. 867–871.
[7] J. Gotman, “Automatic seizure detection: improvements and evaluation,” Electroencephalography and clinical Neurophysiology, vol. 76, no. 4, pp. 317–324, 1990.
[8] L. Boubchir, S. Al-Maadeed, A. Bouridane, and A. A. Chérif, “Classification of EEG signals for detection of epileptic seizure activities based on LBP descriptor of time-frequency images,” in 2015 IEEE International Conference on Image Processing (ICIP), 2015, pp. 3758–3762.
[9] L. Boubchir, S. Al-Maadeed, and A. Bouridane, “On the use of time-frequency features for detecting and classifying epileptic seizure activities in non-stationary EEG signals,” in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014, pp. 5889–5893.
[10] M. Mohammadi, N. A. Khan, and A. A. Pouyan, “Automatic seizure detection using a highly adaptive directional time-frequency distribution,” Multidimensional Systems and Signal Processing, vol. 29, no. 4, pp. 1661–1678, 2018.
[11] L. Boubchir and B. Boashash, “Wavelet denoising based on the MAP estimation using the BKF prior with application to images and EEG signals,” IEEE Transactions on signal processing, vol. 61, no. 8, pp. 1880–1894, 2013.
[12] J. Löfhede, M. Thordstein, N. Löfgren, A. Flisberg, M. Rosa-Zurera, I. Kjellmer, and K. Lindecrantz, “Automatic classification of background EEG activity in healthy and sick neonates.,” J Neural Eng, vol. 7, no. 1, p. 16007, 2010.
[13] B. R. Greene, S. Faul, W. P. Marnane, G. Lightbody, I. Korotchikova, and G. B. Boylan, “A comparison of quantitative EEG features for neonatal seizure detection.,” Clin Neurophysiol, vol. 119, no. 6, pp. 1248–61, 2008.
[14] L. Boubchir, B. Daachi, and V. Pangracious, “A review of feature extraction for EEG epileptic seizure detection and classification,” in Telecommunications and Signal Processing (TSP), 2017 40th International Conference on, 2017, pp. 456–460.
[15] A. Aarabi, F. Wallois, and R. Grebe, “Automated neonatal seizure detection: a multistage classification system through feature selection based on relevance and redundancy analysis,” Clinical Neurophysiology, vol. 117, no. 2, pp. 328–340, 2006.
[16] F. Redelico, F. Traversaro, M. Garc’\ia, W. Silva, O. Rosso, and M. Risk, “Classification of normal and pre-ictal eeg signals using permutation entropies and a generalized linear model as a classifier,” Entropy, vol. 19, no. 2, p. 72, 2017.
[17] M. Mohammadi, A. A. Pouyan, N. A. Khan, and V. Abolghasemi, “Locally optimized adaptive directional time-frequency distributions,” Circuits, Systems, and Signal Processing, vol. 37, no. 8, pp. 3154–3174, 2018.
[18] L. Boubchir, S. Al-Maadeed, and A. Bouridane, “Effectiveness of combined time-frequency imageand signal-based features for improving the detection and classification of epileptic seizure activities in EEG signals,” in 2014 International Conference on Control, Decision and Information Technologies (CoDIT), 2014, pp. 673–678.
[19] R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E. Elger, “Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state,” Physical Review E, vol. 64, no. 6, p. 061907, 2001.
[20] L. Boubchir, S. Al-Maadeed, and A. Bouridane, “Haralick feature extraction from time-frequency images for epileptic seizure detection and classification of EEG data,” in 2014 26th International Conference on Microelectronics (ICM), 2014, pp. 32–35.
[21] N. Kannathal, M. L. Choo, U. R. Acharya, and P. K. Sadasivan, “Entropies for detection of epilepsy in EEG,” Computer methods and programs in biomedicine, vol. 80, no. 3, pp. 187–94, 2005.
[22] K. Polat and S. Güne\cs, “Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform,” Applied Mathematics and Computation, vol. 187, no. 2, pp. 1017–1026, 2007.
[23] A. SUBASI, “EEG signal classification using wavelet feature extraction and a mixture of expert model,” Expert Systems with Applications, vol. 32, no. 4, pp. 1084–1093, 2007.
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Published
2019-07-25
How to Cite
Mohammad, M., & Hussen, H. M. (2019). The State of the Art in Feature Extraction Methods for EEG Classification. UHD Journal of Science and Technology, 3(2), 16–23. https://doi.org/10.21928/uhdjst.v3n2y2019.pp16-23
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