Abstract
EEG-based brain-machine interfaces offer an alternative means of interaction with the environment relying solely on interpreting brain activity. They can not only significantly improve the life quality of people with neuromuscular disabilities, but also present a wide range of opportunities for industrial and commercial applications. This work focuses on the development of a real-time brain-machine interface based on processing and classification of motor imagery EEG signals. The goal was to develop a fast and reliable system that can function in everyday noisy environments. To achieve this, various filtering, feature extraction, and classification methods were tested on three data sets, two of which were recorded in a noisy public setting. Results suggested that the tested linear classifier, paired with band power features, offers higher robustness and similar prediction accuracy, compared to a non-linear classifier based on recurrent neural networks. The final configuration was also successfully tested on a real-time system.
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
McFarland, D., Wolpaw, J.: EEG-based brain-computer interfaces. Current Opinion Biomed. Eng. 4, 194–200 (2017)
Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. Official J. Int. Fed. Clin. Neurophysiol. 113(6), 767–791 (2002)
Schomer, D.L., Lopes da Silva, F.H.: Electroencephalography: basic principles, clinical applications, and related fields. Lippincott Williams & Wilkins, Philadelphia (2011)
Pfurtscheller, G., Lopes Da Silva, F.H.: Event-related EEG/MEG synchronization and desynchronization: Basic principles. Clin. Neurophysiol. 110(11), 1842–1857 (1999)
Pfurtscheller, G., Neuper, C.: Dynamics of sensorimotor oscillations in a motor task. In: Graimann, B., Pfurtscheller, G., Allison, B. (eds.) Brain-Computer Interfaces. The Frontiers Collection. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02091-9_3
Blankertz, B., et al.: The Berlin Brain-Computer Interface presents the novel mental typewriter Hex-o-Spell. In: 3rd International BCI Workshop and Training Course, pp. 2–3. Graz (2006)
Pfurtscheller, G., Brunner, C., Leeb, R., Scherer, R.: The graz brain-computer interface. In: Graimann, B., Pfurtscheller, G., Allison, B. (eds.) Brain-Computer Interfaces. The Frontiers Collection. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02091-9_5
Wolpaw, J.R., McFarland, D.J., Vaughan, T.M.: Brain-computer interface research at the Wadsworth Center. IEEE Trans. Rehabil. Eng. 8(2), 222–226 (2000)
Prakaksita, N., Kuo, C.Y., Kuo, C.H.: Development of a motor imagery based brain-computer interface for humanoid robot control applications. In: 2016 IEEE International Conference on Industrial Technology (ICIT), pp. 1607–1613. IEEE, Taipei (2016)
Tibor Schirrmeister, R., et al.: Deep learning with convolutional neural networks for EEG decoding and visualization. Hum. Brain Mapp. 38, 5391–5420 (2017)
Brunner, C., Leeb, R., Müller-Putz, G., Schlögl, A., Pfurtscheller, G.: BCI Competition 2008 - Graz data set A. Graz (2008)
Naeem, M., Brunner, C., Leeb, R., Graimann, B., Pfurtscheller, G.: Seperability of four-class motor imagery data using independent components analysis. J. Neural Eng. 3, 208–216 (2006)
Klem, G.H., Lüders, H.O., Jasper, H.H., Elger, C.: The ten-twenty electrode system of the International Federation. Electroencephalogr. Clin. Neurophysiol. Suppl. 52, 2–5 (1999)
Widmann, A., Schröger, E., Maess, B.: Digital filter design for electrophysiological data - a practical approach. J. Neurosci. Methods 250, 34–46 (2015)
Alessio, S.M.: Digital Signal Processing and Spectral Analysis for Scientists. Springer International Publishing, Switzerland (2016)
Pfurtscheller, G., et al.: Current trends in graz brain-computer interface (BCI) research. IEEE Trans. Biomed. Eng. 8(2), 216–219 (2000)
Hjorth, B.: EEG analysis based on time domain properties. Electroencephalogr. Clin. Neurophysiol. 29(3), 306–310 (1981)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2009)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2011)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: 3rd International Conference for Learning Representations. San Diego (2015)
Slovenian Initiative for National Grid (SLING). http://www.sling.si
Berger Neurorobotics. http://bergerneurorobotics.com/
Sun, S., Zhou, J.: A review of adaptive feature extraction and classification methods for EEG-based brain-computer interfaces. In: Proceedings of the International Joint Conference on Neural Networks, pp. 1746–1753. IEEE, Beijing (2014)
Acknowledgement
This work is supported by Centre for BioRobotics (CBR) at University of Southern Denmark (SDU, Denmark) and Horizon 2020 Framework Programme (FETPROACT-01-2016–FET Proactive: emerging themes and communities) under grant agreement no. 732266 (Plan4Act).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Gorjup, G., Vrabič, R., Stoyanov, S.P., Andersen, M.Ø., Manoonpong, P. (2018). Development of a Real-Time Motor-Imagery-Based EEG Brain-Machine Interface. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11307. Springer, Cham. https://doi.org/10.1007/978-3-030-04239-4_55
Download citation
DOI: https://doi.org/10.1007/978-3-030-04239-4_55
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04238-7
Online ISBN: 978-3-030-04239-4
eBook Packages: Computer ScienceComputer Science (R0)