Abstract
The decoding of brain signals is a fundamental component of a brain-computer interface. Despite the success of deep convolutional neural networks (CNNs) in other fields, only recently these techniques have been applied to electroencephalographic (EEG) signals. One drawback of CNNs is the lack of interpretation of the learned features. In this study we introduce for the first time a sinc-convolutional layer into a CNN for EEG motor execution decoding, allowing a straightforward interpretation of the learned kernels. Furthermore, we apply a gradient-based analysis to assess the most relevant EEG bands for each movement and the most relevant EEG electrodes exploited in these bands. In addition to a slight accuracy improvement from 91.9 to 92.4%, our results suggest that the \(high\gamma \) band is the most relevant EEG band, with gradient-based scalp distributions well localized at specific subsets of electrodes.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Wolpaw, J., Wolpaw, E.: Brain-Computer Interfaces: Principles and Practice. Oxford University Press, USA (2012)
Chaudhary, U., Birbaumer, N., Ramos-Murguialday, A.: Brain-computer interfaces for communication and rehabilitation. Nat. Rev. Neurol. 12, 513–525 (2016)
Nicolas-Alonso, L.F., Gomez-Gil, J.: Brain computer interfaces, a review. Sensors (Basel, Switzerland) 12(2), 1211–79 (2012)
Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W., Ball, T.: Deep learning with convolutional neural networks for EEG decoding and visualization. Hum. Brain Mapp. 38(11), 5391–5420 (2017)
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)
Cecotti, H., Graser, A.: Convolutional neural networks for p300 detection with application to brain-computer interfaces. IEEE Trans. Pattern Anal. Mach. Intell. 33(3), 433–445 (2011)
Hajinoroozi, M., Mao, Z., Jung, T.P., Lin, C.T., Huang, Y.: Eeg-based prediction of driver’s cognitive performance by deep convolutional neural network. Signal Process. Image Commun. 47, 549–555 (2016)
van Leeuwen, K., Sun, H., Tabaeizadeh, M., Struck, A., van Putten, M., Westover, M.: Detecting abnormal electroencephalograms using deep convolutional networks. Clin. Neurophysiol. 130(1), 77–84 (2019)
Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint (2013)
Ravanelli, M., Bengio, Y.: Speaker recognition from raw waveform with sincnet. In: 2018 IEEE Spoken Language Technology Workshop (SLT), pp. 1021–1028, December 2018
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning, Proceedings of Machine Learning Research, vol. 37, pp. 448–456. PMLR, Lille, France, 07–09 July 2015
Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint (2015)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint (2014)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Borra, D., Fantozzi, S., Magosso, E. (2020). EEG Motor Execution Decoding via Interpretable Sinc-Convolutional Neural Networks. In: Henriques, J., Neves, N., de Carvalho, P. (eds) XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019. MEDICON 2019. IFMBE Proceedings, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-31635-8_135
Download citation
DOI: https://doi.org/10.1007/978-3-030-31635-8_135
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31634-1
Online ISBN: 978-3-030-31635-8
eBook Packages: EngineeringEngineering (R0)