Abstract
Automated electroencephalographic (EEG) signals classification using deep learning algorithms is an emerging technique in neuroscience that has the potential to detect brain pathologies such as epilepsy efficiently. In this process, deep learning algorithms are trained with labeled EEG signal datasets. However, due to the highly complex nature of EEG signals and the large amount of irrelevant information they contain, feature extraction techniques must be applied to reduce their dimensionality and focus on relevant information. This paper presents a comparative study on feature extraction methods for the classification of EEG recordings. The results demonstrate that the proposed classification algorithms and characterisation techniques are effective and suitable, as the accuracy metrics reach a value of 99.27%. The results presented in this paper contribute to the further development of automatic EEG signal classification methods based on deep learning.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alturki, F.A., AlSharabi, K., Abdurraqeeb, A.M., Aljalal, M.: EEG signal analysis for diagnosing neurological disorders using discrete wavelet transform and intelligent techniques. Sensors 20(9) (2020). https://doi.org/10.3390/s20092505.http://www.mdpi.com/1424-8220/20/9/2505
Amin, H.U., et al.: Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques. Australas. Phys. Eng. Sci. Med. 38(1), 139–149 (2015)
Asanza, V., Sánchez-Pozo, N.N., Lorente-Leyva, L.L., Peluffo-Ordóñez, D.H., Loayza, F.R., Peláez, E.: Classification of subjects with Parkinson’s disease using finger tapping dataset. IFAC-PapersOnLine 54(15), 376–381 (2021)
Bairagi, R.N., Maniruzzaman, M., Pervin, S., Sarker, A.: Epileptic seizure identification in EEG signals using DWT, ANN and sequential window algorithm. Soft Comput. Lett. 3, 100026 (2021)
Burleigh, T.L., Griffiths, M.D., Sumich, A., Wang, G.Y., Kuss, D.J.: Gaming disorder and internet addiction: a systematic review of resting-state EEG studies. Addict. Behav. 107, 106429 (2020)
Craik, A., He, Y., Contreras-Vidal, J.L.: Deep learning for electroencephalogram (EEG) classification tasks: a review. J. Neural Eng. 16(3), 031001 (2019)
Fıçıcı, C., Telatar, Z., Eroğul, O.: Automated temporal lobe epilepsy and psychogenic nonepileptic seizure patient discrimination from multichannel EEG recordings using dwt based analysis. Biomed. Sig. Process. Control 77, 103755 (2022)
Ghosh, S., Das, P., Nandi, S.: Transfer learning-based deep convolutional neural network for motor imagery EEG classification. J. Ambient Intell. Humanized Comput. 9(5), 1669–1685 (2018). https://doi.org/10.1007/s12652-018-0858-z
Hamm, C.A., et al.: Deep learning for liver tumor diagnosis part i: development of a convolutional neural network classifier for multi-phasic MRI. Eur. Radiol. 29, 3338–3347 (2019)
Hassouneh, A., Mutawa, A., Murugappan, M.: Development of a real-time emotion recognition system using facial expressions and EEG based on machine learning and deep neural network methods. Inf. Med. Unlocked 20, 100372 (2020)
Iscan, Z., Dokur, Z., Demiralp, T.: Classification of electroencephalogram signals with combined time and frequency features. Expert Syst. Appl. 38(8), 10499–10505 (2011)
Islam, M.K., Rastegarnia, A.: Recent advances in EEG (non-invasive) based BCI applications. Front. Comput. Neurosci. (2023)
Jemal, I., Mezghani, N., Abou-Abbas, L., Mitiche, A.: An interpretable deep learning classifier for epileptic seizure prediction using EEG data. IEEE Access 10, 60141–60150 (2022)
Liu, Y.H., et al.: Epilepsy detection with artificial neural network based on as-fabricated neuromorphic chip platform. AIP Adv. 12(3), 035106 (2022)
Mahjoub, C., Jeannès, R.L.B., Lajnef, T., Kachouri, A.: Epileptic seizure detection on EEG signals using machine learning techniques and advanced preprocessing methods. Biomed. Eng./Biomed. Tech. 65(1), 33–50 (2020)
Mancha, V.R., Srinivasa, R.E., Ch, S.: Advanced convolutional neural network classification for automatic seizure epilepsy detection in EEG signal. IOP Conf. Ser.: Mater. Sci. Eng. 1074(1), 012005 (2021)
Ouichka, O., Echtioui, A., Hamam, H.: Deep learning models for predicting epileptic seizures using iEEG signals. Electronics 11(4), 605 (2022)
Saeed, H., Mohammadi, K.: A novel EEG feature extraction method using multi-objective optimization. Biomed. Sig. Process. Control 33, 1–10 (2017). https://doi.org/10.1016/j.bspc.2016.10.005
Saeidi, M., et al.: Neural decoding of EEG signals with machine learning: a systematic review. Brain Sci. 11(11), 1525 (2021)
Shoeibi, A., et al.: An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: methods, challenges, and future works. Comput. Biol. Med. 106053 (2022)
Shoka, A., Dessouky, M., El-Sherbeny, A., El-Sayed, A.: Literature review on EEG preprocessing, feature extraction, and classifications techniques. Menoufia J. Electron. Eng. Res 28(1), 292–299 (2019)
Singh, K., Malhotra, J.: Smart neurocare approach for detection of epileptic seizures using deep learning based temporal analysis of EEG patterns. Multimed. Tools Appl. 81(20), 29555–29586 (2022)
Tohidi, M., Madsen, J.K., Moradi, F.: Low-power high-input-impedance EEG signal acquisition SoC with fully integrated IA and signal-specific ADC for wearable applications. IEEE Trans. Biomed. Circ. Syst. 13(6), 1437–1450 (2019)
Tuncer, E., Bolat, E.D.: Channel based epilepsy seizure type detection from electroencephalography (EEG) signals with machine learning techniques. Biocybernetics Biomed. Eng. 42(2), 575–595 (2022)
Wang, F., et al.: Motor imagery classification using geodesic filtering common spatial pattern and filter-bank feature weighted support vector machine. Rev. Sci. Instrum. 91(3), 034106 (2020)
Wu, J., Liu, H., Gao, X.: A semi-supervised deep clustering framework for EEG based motor imagery task. IEEE Trans. Neural Netw. Learn. Syst. 30(12), 3663–3673 (2019). https://doi.org/10.1109/TNNLS.2019.2909198
Zarei, A., Asl, B.M.: Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy based features of EEG signals. Comput. Biol. Med. 131, 104250 (2021)
Zeng, W., Li, M., Yuan, C., Wang, Q., Liu, F., Wang, Y.: Identification of epileptic seizures in EEG signals using time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks. Artif. Intell. Rev. 53(4), 3059–3088 (2020)
Zhang, X., Zhou, W., Li, Y., Li, L.: Combining deep belief network and support vector machine to classify motor imagery EEG signal. Neurocomputing 173, 1500–1508 (2016). https://doi.org/10.1016/j.neucom.2015.09.080
Zheng, X., Chen, W., You, Y., Jiang, Y., Li, M., Zhang, T.: Ensemble deep learning for automated visual classification using EEG signals. Pattern Recognit. 102, 107147 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sánchez-Pozo, N.N., Lascano-Rivera, S., Montalvo-Marquez, F.J., Ortiz-Reinoso, D.Y. (2023). Deep Learning for Automatic Electroencephalographic Signals Classification. In: Rojas, I., Valenzuela, O., Rojas Ruiz, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2023. Lecture Notes in Computer Science(), vol 13919. Springer, Cham. https://doi.org/10.1007/978-3-031-34953-9_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-34953-9_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34952-2
Online ISBN: 978-3-031-34953-9
eBook Packages: Computer ScienceComputer Science (R0)