Abstract
Nowadays, machine and deep learning techniques are widely used in different areas, ranging from economics to biology. In general, these techniques can be used in two ways: trying to adapt well-known models and architectures to the available data, or designing custom architectures. In both cases, to speed up the research process, it is useful to know which type of models work best for a specific problem and/or data type. By focusing on EEG signal analysis, and for the first time in literature, in this paper a benchmark of machine and deep learning for EEG signal classification is proposed. For our experiments we used the four most widespread models, i.e., multilayer perceptron, convolutional neural network, long short-term memory, and gated recurrent unit, highlighting which one can be a good starting point for developing EEG classification models.
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
Similar content being viewed by others
References
Avola, D., Cinque, L., Foresti, G.L., Martinel, N., Pannone, D., Piciarelli, C.: Low-Level Feature Detectors and Descriptors for Smart Image and Video Analysis: A Comparative Study, pp. 7–29 (2018)
Avola, D., Bernardi, M., Cinque, L., Foresti, G.L., Massaroni, C.: Adaptive bootstrapping management by keypoint clustering for background initialization. Patt. Recogn. Lett. 100, 110–116 (2017)
Avola, D., Cinque, L.: Encephalic NMR tumor diversification by textural interpretation. In: Proceedings of the 15th International Conference on Image Analysis and Processing (ICIAP), pp. 394–403 (2009)
Avola, D., Cinque, L., Fagioli, A., Filetti, S., Grani, G., Rodolà, E.: Multimodal feature fusion and knowledge-driven learning via experts consult for thyroid nodule classification. IEEE Trans. Circ. Syst. Video Technol., 1–8 (2021)
Avola, D., Cinque, L., Fagioli, A., Foresti, G.L., Massaroni, C., Pannone, D.: Feature-based SLAM algorithm for small scale UAV with Nadir view. In: Ricci, E., Rota Bulò, S., Snoek, C., Lanz, O., Messelodi, S., Sebe, N. (eds.) ICIAP 2019. LNCS, vol. 11752, pp. 457–467. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30645-8_42
Avola, D., Cinque, L., Foresti, G.L., Pannone, D.: Automatic deception detection in RGB videos using facial action units. In: Proceedings of the 13th International Conference on Distributed Smart Cameras, pp. 1–6 (2019)
Avola, D., Foresti, G.L., Cinque, L., Massaroni, C., Vitale, G., Lombardi, L.: A multipurpose autonomous robot for target recognition in unknown environments. In: Proceedings of the 14th International Conference on Industrial Informatics (INDIN), pp. 766–771 (2016)
Chen, J., Jiang, D., Zhang, Y.: A hierarchical bidirectional GRU model with attention for EEG-based emotion classification. IEEE Access 7, 118530–118540 (2019)
Dong, S., Wang, P., Abbas, K.: A survey on deep learning and its applications. Comput. Sci. Rev. 40, 1–22 (2021)
Gandhi, T., Panigrahi, B.K., Anand, S.: A comparative study of wavelet families for EEG signal classification. Neurocomputing 74(17), 3051–3057 (2011)
Garrett, D., Peterson, D.A., Anderson, C.W., Thaut, M.H.: Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 141–144 (2003)
Jiang, X., Bian, G.B., Tian, Z.: Removal of artifacts from EEG signals: a review. Sensors 19(5), 987 (2019)
Lawhern, V.J., Solon, A.J., Waytowich, N.R., Gordon, S.M., Hung, C.P., Lance, B.J.: EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces. J. Neural Eng. 15(5), 056013 (2018)
Li, Y., Wang, X.D., Luo, M.L., Li, K., Yang, X.F., Guo, Q.: Epileptic seizure classification of EEGs using time-frequency analysis based multiscale radial basis functions. IEEE J. Biomed. Health Inform. 22(2), 386–397 (2017)
Luo, T.J., Chao, F., et al.: Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network. BMC Bioinform. 19(1), 1–18 (2018)
Nguyen, C.H., Karavas, G.K., Artemiadis, P.: Inferring imagined speech using EEG signals: a new approach using Riemannian manifold features. J. Neural Eng. 15(1), 016002 (2017)
Patrício, D.I., Rieder, R.: Computer vision and artificial intelligence in precision agriculture for grain crops: a systematic review. Comput. Electron. Agric. 153, 69–81 (2018)
Petracca, A., et al.: A virtual ball task driven by forearm movements for neuro-rehabilitation. In: Proceedings of the International Conference on Virtual Rehabilitation (ICVR), pp. 162–163 (2015)
Pham, T., Ma, W., Tran, D., Nguyen, P., Phung, D.: EEG-based user authentication in multilevel security systems. In: Advanced Data Mining and Applications, pp. 513–523 (2013)
Rabcan, J., Levashenko, V., Zaitseva, E., Kvassay, M.: Review of methods for EEG signal classification and development of new fuzzy classification-based approach. IEEE Access 8, 189720–189734 (2020)
Ruhunage, I., Perera, C.J., Nisal, K., Subodha, J., Lalitharatne, T.D.: EMG signal controlled Transhumerai prosthetic with EEG-SSVEP based approach for hand open/close. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3169–3174 (2017)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. California Univ San Diego La Jolla Inst for Cognitive Science, Technical report (1985)
Schalk, G., McFarland, D., Hinterberger, T., Birbaumer, N., Wolpaw, J.: BCI 2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51(6), 1034–1043 (2004)
Stone, J.V.: Independent component analysis: an introduction. Trends Cogn. Sci. 6(2), 59–64 (2002)
Tabar, Y.R., Halici, U.: A novel deep learning approach for classification of EEG motor imagery signals. J. Neural Eng. 14(1), 016003 (2016)
Thimm, G., Fiesler, E.: High-order and multilayer perceptron initialization. IEEE Trans. Neural Netw. 8(2), 349–359 (1997)
Wang, M., Abdelfattah, S., Moustafa, N., Hu, J.: Deep gaussian mixture-hidden Markov model for classification of EEG signals. IEEE Trans. Emerg. Top. Comput. Intell. 2(4), 278–287 (2018)
Wang, P., Jiang, A., Liu, X., Shang, J., Zhang, L.: LSTM-based EEG classification in motor imagery tasks. IEEE Trans. Neural Syst. Rehabil. Eng. 26(11), 2086–2095 (2018)
Xu, G., et al.: A deep transfer convolutional neural network framework for EEG signal classification. IEEE Access 7, 112767–112776 (2019)
Yıldırım, Ö., Baloglu, U.B., Acharya, U.R.: A deep convolutional neural network model for automated identification of abnormal EEG signals. Neural Comput. Appl. 32(20), 15857–15868 (2020)
Zhang, X., Yao, L., Zhang, D., Wang, X., Sheng, Q.Z., Gu, T.: Multi-person brain activity recognition via comprehensive EEG signal analysis. In: Proceedings of the EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous), pp. 28–37 (2017)
Zhou, P.Y., Chan, K.C.: Fuzzy feature extraction for multichannel EEG classification. IEEE Trans. Cogn. Dev. Syst. 10(2), 267–279 (2016)
Acknowledgement
This work was supported by the MIUR under grant “Departments of Excellence 2018–2022" of the Sapienza University Computer Science Department and the ERC Starting Grant no. 802554 (SPECGEO).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Avola, D. et al. (2022). Analyzing EEG Data with Machine and Deep Learning: A Benchmark. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13231. Springer, Cham. https://doi.org/10.1007/978-3-031-06427-2_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-06427-2_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-06426-5
Online ISBN: 978-3-031-06427-2
eBook Packages: Computer ScienceComputer Science (R0)