Abstract
Deep learning approaches have been at the forefront of machine learning problem-solving for the last decade. Although computationally more intensive than traditional techniques, the performance of artificial neural networks has justified their adoption for a wide array of applications. However, for small and high-dimensional datasets the large amount of learnable parameters is often a disadvantage. In this situation, the relationship between model complexity and quality gains relevance, since overfitting issues play a more central role. This is the case for Electroencephalography (EEG) classification, where it is usual to only have a small number of trials comprised of many electrode readings. In this paper, we optimize three Convolutional Neural Networks (CNNs) of different depths and evaluate them on three EEG Motor Imagery (MI) datasets in terms of classification accuracy, while also paying close attention to time consumption. The results show that the shallower ones tend to perform better at a lower cost than the deeper ones, which suggests that efforts in the direction of cost-saving may be aligned with model accuracy for small, high-dimensional datasets such as those often found in EEG.
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References
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/guide
Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adeli, H.: Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput. Biol. Med. 100, 270–278 (2018)
Amin, S.U., Alsulaiman, M., Muhammad, G., Mekhtiche, M.A., Shamim Hossain, M.: Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion. Future Gener. Comput. Syst. 101, 542–554 (2019)
Asensio-Cubero, J., Gan, J.Q., Palaniappan, R.: Multiresolution analysis over simple graphs for brain computer interfaces. J. Neural Eng. 10(4), 046014 (2013)
Benavoli, A., Corani, G., Demšar, J., Zaffalon, M.: Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis. J. Mach. Learn. Res. 18(1), 2653–2688 (2017)
Calvo, B., Santafé Rodrigo, G.: scmamp: statistical comparison of multiple algorithms in multiple problems. R J. 8/1 (2016)
Carrasco, J., García, S., del Mar Rueda, M., Herrera, F.: rNPBST: an R package covering non-parametric and bayesian statistical tests. In: Martínez de Pisón, F.J., Urraca, R., Quintián, H., Corchado, E. (eds.) HAIS 2017. LNCS (LNAI), vol. 10334, pp. 281–292. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59650-1_24
Chiarelli, A.M., Croce, P., Merla, A., Zappasodi, F.: Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification. J. Neural Eng. 15(3), 036028 (2018)
Chollet, F., et al.: Keras (2015). https://keras.io
Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20(1), 37–46 (1960)
Daubechies, I.: Ten Lectures on Wavelets, vol. 61. Siam (1992)
Eiben, A.E., Smith, J.E., et al.: Introduction to Evolutionary Computing, 2 edn., vol. 53. Springer, Heidelberg (2015)
Friedman, M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32(200), 675–701 (1937)
Oliphant, T.E.: A guide to NumPy, vol. 1. Trelgol Publishing USA (2006). https://docs.scipy.org/doc/numpy/reference/
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011). https://scikit-learn.org/stable/documentation.html
Raudys, S., Jain, A.: Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Trans. Pattern Anal. Mach. Intell. 13(3), 252–264 (1991)
Sakhavi, S., Guan, C., Yan, S.: Learning temporal information for brain-computer interface using convolutional neural networks. IEEE Trans. Neural Netw. Learn. Syst. 29(11), 5619–5629 (2018)
Tayeb, Z., et al.: Validating deep neural networks for online decoding of motor imagery movements from EEG signals. Sensors 19(1), 210 (2019)
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)
Acknowledgements
This work was supported by projects PGC2018-098813-B-C31 and PGC2018-098813-B-C32 (Spanish “Ministerio de Ciencia, Innovación y Universidades”), and by European Regional Development Funds (ERDF).
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León, J., Ortiz, A., Damas, M., González, J., Ortega, J. (2020). Cost-Efficiency of Convolutional Neural Networks for High-Dimensional EEG Classification. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_65
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