Abstract
Brain-Computer Interfaces (BCI) have many applications, such as motor rehabilitation in post-stroke situations. In most cases, the BCI captures brain signals and classifies them to determine a command in an electronic system. Given a large number of BCI applications, many models are improving signal classification accuracy. For instance, we proposed the Single Electrode Energy (SEE) to classify motor imagery and won the Clinical BCI Challenge 2020. However, this method uses a single electrode to extract the brain characteristics. Here, we propose a new method, named single feature genetic programming, to create a function for feature extraction in BCI. Our approach assembles more than one electrode in a unique characteristic value. Moreover, we tested the use of a bank of band-pass filter and wavelet to preprocess the data. We evaluate the new approach using the Clinical BCI Challenge 2020 data and compare it with SEE. Our results show that When Single Feature Genetic Programming has a kappa coefficient 18% better than SEE.
The authors thank the financial support provided by CAPES, CNPq (grants 312337/2017-5, 312682/2018-2, 311206/2018-2, and 451203/2019-4), FAPEMIG, FAPESP, and UFJF.
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
Andreu-Perez, Chowdhury, A., Prasad, M.: Clinical BCI Challenge-WCCI2020. https://sites.google.com/view/bci-comp-wcci/
Barbosa, A.O., Achanccaray, D.R., Meggiolaro, M.A.: Activation of a mobile robot through a brain computer interface. In: IEEE International Conference on Robotics and Automation, pp. 4815–4821 (2010)
Bell, C.J., Shenoy, P., Chalodhorn, R., Rao, R.P.: Control of a humanoid robot by a noninvasive brain-computer interface in humans. J. Neural Eng. 5(2), 214 (2008)
Broyden, C.G.: The convergence of a class of double-rank minimization algorithms 1. general considerations. IMA J. Appl. Math. 6(1), 76–90 (1970)
Buch, E., et al.: Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke 39(3), 910–917 (2008)
Edlinger, G., Holzner, C., Guger, C.: A hybrid brain-computer interface for smart home control. In: Jacko, J.A. (ed.) HCI 2011. A hybrid brain-computer interface for smart home control, vol. 6762, pp. 417–426. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21605-3_46
Emigdio, Z., Trujillo, L., Legrand, P., Faïta-Aïnseba, F., et al.: Eeg feature extraction using genetic programming for the classification of mental states. Algorithms 13(9), 221 (2020)
Fortin, F.A., De Rainville, F.M., Gardner, M.A., Parizeau, M., Gagné, C.: DEAP: evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171–2175 (2012)
Islam, M.K., Rastegarnia, A., Yang, Z.: Methods for artifact detection and removal from scalp EEG: a review. Neurophysiologie Clinique/Clin. Neurophysiol. 46(4–5), 287–305 (2016)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press (1992)
Lee, W.T., Nisar, H., Malik, A.S., Yeap, K.H.: A brain computer interface for smart home control. In: IEEE International Symposium on Consumer Electronics, pp. 35–36 (2013)
Miranda, Í.M., Aranha, C., Ladeira, M.: Classification of EEG signals using genetic programming for feature construction. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1275–1283 (2019)
Nicolas-Alonso, L.F., Gomez-Gil, J.: Brain computer interfaces, a review. Sensors 12(2), 1211–1279 (2012)
O’Regan, S., Faul, S., Marnane, W.: Automatic detection of EEG artefacts arising from head movements using EEG and gyroscope signals. Med. Eng. Phys. 35(7), 867–874 (2013)
Poli, R., Salvaris, M., Cinel, C.: Evolution of a brain-computer interface mouse via genetic programming. In: Silva, S., Foster, J.A., Nicolau, M., Machado, P., Giacobini, M. (eds.) EuroGP 2011. LNCS, vol. 6621, pp. 203–214. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20407-4_18
Silvoni, S., et al.: Brain-computer interface in stroke: a review of progress. Clin. EEG Neurosci. 42(4), 245–252 (2011)
de Souza, G.H., Bernardino, H.S., Vieira, A.B., Barbosa, H.J.C.: Differential evolution based spatial filter optimization for brain-computer interface. In: Proceedings of the ACM Genetic and Evolutionary Computation Conference, pp. 1165–1173 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
de Souza, G.H., Bernardino, H.S., Vieira, A.B., Barbosa, H.J.C. (2021). Genetic Programming for Feature Extraction in Motor Imagery Brain-Computer Interface. In: Marreiros, G., Melo, F.S., Lau, N., Lopes Cardoso, H., Reis, L.P. (eds) Progress in Artificial Intelligence. EPIA 2021. Lecture Notes in Computer Science(), vol 12981. Springer, Cham. https://doi.org/10.1007/978-3-030-86230-5_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-86230-5_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86229-9
Online ISBN: 978-3-030-86230-5
eBook Packages: Computer ScienceComputer Science (R0)