Abstract
The classification of EEG signals provides an important element of brain-computer interface (BCI) applications, underlying an efficient interaction between a human and a computer application. The BCI applications can be especially useful for people with disabilities. Numerous experiments aim at recognition of motion intent of left or right hand being useful for locked-in-state or paralyzed subjects in controlling computer applications. The chapter presents an experimental study of several methods for real motion and motion intent classification (rest/upper/lower limbs motion, and rest/left/right hand motion). First, our approach to EEG recordings segmentation and feature extraction is presented. Then, 5 classifiers (Naïve Bayes, Decision Trees, Random Forest, Nearest-Neighbors NNge, Rough Set classifier) are trained and tested using examples from an open database. Feature subsets are selected for consecutive classification experiments, reducing the number of required EEG electrodes. Methods comparison and obtained results are presented, and a study of features feeding the classifiers is provided. Differences among participating subjects and accuracies for real and imaginary motion are discussed. It is shown that though classification accuracy varies from person to person, it could exceed 80% for some classifiers.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alotaiby, T., El-Samie, F.E., Alshebeili S.A.: A review of channel selection algorithms for eeg signal processing. EURASIP. J. Adv. Signal Process, 66 (2015)
BCI2000. Bci2000 instrumentation system project. http://www.bci2000.org, Accessed on 2017-03-01
Bek, J., Poliakoff, E., Marshall, H., Trueman, S., Gowen, E.: Enhancing voluntary imitation through attention and motor imagery. Exp. Brain Res. 234, 1819–1828 (2016)
Bhattacharyya, S., Konar, A., Tibarewala, D.N.: Motor imagery, p300 and error-related eeg-based robot arm movement control for rehabilitation purpose. Med. Biol. Eng. Comput. 52, 2014 (1007)
Chen, S., Lai, Y.A.: Sgnal-processing-based technique for p300 evoked potential detection with the applications into automated character recognition. EURASIP. J. Adv. Signal Process. 152 (2014)
Choi, K.: Electroencephalography (eeg)-based neurofeedback training for brain-computer interface (bci). Exp. Brain Res. 231, 351–365 (2013)
Corralejo, R., Nicolas-Alonso, L.F., Alvarez, D., Hornero, R.: A p300-based brain-computer interface aimed at operating electronic devices at home for severely disabled people. Med. Biol. Eng. Comput. 52, 861–872 (2014)
Czyżewski, A., Kostek, B., Kurowski, A., Szczuko, P., Lech, M., Odya, P., Kwiatkowska, A.: Assessment of hearing in coma patients employing auditory brainstem response, electroencephalography and eye-gaze-tracking. In: Proceedings of the 173rd Meeting of the Acoustical Society of America (2017)
Dickhaus, T., Sannelli, C., Muller, K.R., Curio, G., Blankertz, B.: Predicting bci performance to study bci illiteracy. BMC Neurosci. 10 (2009)
Diez, P.F., Mut, V.A., Avila Perona, E.M.: Asynchronous bci control using high-frequency. SSVEP. J. NeuroEngineering. Rehabil. 8(39) (2011)
Doud, A.J., Lucas, J.P., Pisansky, M.T., He, B.: Continuous three-dimensional control of a virtual helicopter using a motor imagery based brain-computer interface. PLoS ONE. 6(10) (2011)
Faller, J., Scherer, R., Friedrich, E., Costa, U., Opisso, E., Medina, J., Muller-Putz, G.R.: Non-motor tasks improve adaptive brain-computer interface performance in users with severe motor impairment. Front. Neurosci., 8 (2014)
Gardener, M., Beginning, R.: The statistical programming language, (2012). https://cran.r-project.org/manuals.html, Accessed on 2017-03-01
Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101, 215–220 (2000)
He, B., Gao, S., Yuan, H., Wolpaw, JR.: Brain-computer interfaces, In: He, B. (ed.) Neural Engineering, pp. 87–151 (2012). https://doi.org/10.1007/978-1-4614-5227-0_2
Iscan, Z.: Detection of p300 wave from eeg data for brain-computer interface applications. Pattern Recognit. Image Anal. 21(481) (2011)
Janusz, A., Stawicki, S.: Applications of approximate reducts to the feature selection problem. In: Proceedings of the International Conference on Rough Sets and Knowledge Technology (RSKT), number 6954 in Lecture Notes in Artificial Intelligence, pp. 45–50 (2011)
John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, pp. 338–345 (1995)
Jung, T.P., Makeig, S., Humphries, C., Lee, T.W., McKeown, M.J., Iragui, V., Sejnowski, T.J.: Removing electroencephalographic artifacts by blind source separation. Psychophysiology 37, 163–178 (2000)
Kasahara, T., Terasaki, K., Ogawa, Y.: The correlation between motor impairments and event-related desynchronization during motor imagery in als patients. BMC Neurosci. 13(66) (2012)
Kayikcioglu, T., Aydemir, O.: A polynomial fitting and k-nn based approach for improving classification of motor imagery bci data. Pattern Recognit. Lett. 31(11), 1207–1215 (2010)
Krepki, R., Blankertz, B., Curio, G., Muller, K.R.: The berlin brain-computer interface (bbci) - towards a new communication channel for online control in gaming applications. Multimed. Tools Appl. 33, 73–90 (2007)
Kumar, S.U., Inbarani, H.: Pso-based feature selection and neighborhood rough set-based classification for bci multiclass motor imagery task. Neural Comput. Appl. 33, 1–20 (2016)
LaFleur, K., Cassady, K., Doud, A.J., Shades, K., Rogin, E., He, B.: Quadcopter control in three-dimensional space using a noninvasive motor imagery based brain-computer interface. J. Neural. Eng. 10 (2013)
Leeb, R., Pfurtscheller, G.: Walking through a virtual city by thought. In: Proceedings of the 26th Annual International Conference of the IEEE EMBS, (2004)
Leeb, R., Scherer, R., Lee, F., Bischof, H., Pfurtscheller, G.: Navigation in virtual environments through motor imagery. In: Proceedings of the 9th Computer Vision Winter Workshop, pp. 99–108, (2004)
Marple, S.L.: Computing the discrete-time analytic signal via fft. IEEE Trans. Signal Proc. 47, 2600–2603 (1999)
Martin, B.: Instance-based learning: nearest neighbour with generalization. Technical report, University of Waikato, Department of Computer Science, Hamilton, New Zealand (1995)
Nakayashiki, K., Saeki, M., Takata, Y.: Modulation of event-related desynchronization during kinematic and kinetic hand movements. J. NeuroEng. Rehabil. 11(90) (2014)
Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)
Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. Proc. of IEEE 89, 1123–1134 (2001)
Pfurtscheller, G., Brunner, C., Schlogl, A., Lopes, F.H.: Mu rhythm (de)synchronization and eeg single-trial classification of different motor imagery tasks. NeuroImage 31, 153–159 (2006)
Postelnicu, C., Talaba, D.: P300-based brain-neuronal computer interaction for spelling applications. IEEE Trans. Biomed. Eng. 60, 534–543 (2013)
Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann (1993)
Riza, S.L., Janusz, A., Slezak, D., Cornelis, C., Herrera, F., Benitez, J.M., Bergmeir, C., Stawicki, S.; Roughsets: data analysis using rough set and fuzzy rough set theories, (2015). https://github.com/janusza/RoughSets, Accessed on 2017-03-01
Roy, S.: Nearest neighbor with generalization. Christchurch, New Zealand (2002)
Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: Bci 2000: A general-purpose brain-computer interface (bci) system. IEEE Trans. Biomed. Eng. 51, 1034–1043 (2004)
Schwarz, A., Scherer, R., Steyrl, D., Faller, J., Muller-Putz, G.: Co-adaptive sensory motor rhythms brain-computer interface based on common spatial patterns and random forest. In: Proceedings of the 37th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), (2015)
Shan, H., Xu, H., Zhu, S., He, B.: A novel channel selection method for optimal classification in different motor imagery bci paradigms. BioMed. Eng. OnLine, 14 (2015)
Silva, J., Torres-Solis, J., Chau, T.: A novel asynchronous access method with binary interfaces. J. NeuroEng. Rehabil. 5(24) (2008)
Siuly, S., Li, Y.: Improving the separability of motor imagery eeg signals using a cross correlation-based least square support vector machine for brain computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 20(4), 526–538 (2012)
Siuly, S., Wang, H., Zhang, Y.: Detection of motor imagery eeg signals employing naive bayes based learning process. J. Measurement 86, 148–158 (2016)
Suh, D., Sang Cho, H., Goo, J., Park, K.S., Hahn, M.: Virtual navigation system for the disabled by motor imagery. In: Advances in Computer, Information, and Systems Sciences, and Engineering, pp. 143–148 (2006). https://doi.org/10.1007/1-4020-5261-8_24
Szczuko, P., Lech, M., Czyżewski, A.: Comparison of methods for real and imaginary motion classification from eeg signals. In: Proceedings of ISMIS conference, (2017)
Szczuko, P.: Real and imagery motion classification based on rough set analysis of eeg signals for multimedia applications. Multimed. Tools Appl. (2017). https://doi.org/10.1007/s11042-017-4458-7
Szczuko, P.: Rough set-based classification of eeg signals related to real and imagery motion. In: Proceedings Signal Processing Algorithms, Architectures, Arrangements, and Applications, (2016)
Tadel, F., Baillet, S., Mosher, J.C., Pantazis, D., Leahy, R.M.: Brainstorm: A user-friendly application for meg/eeg analysis. Comput. Intell. Neurosci. vol. 2011, Article ID 879716 (2011). https://doi.org/10.1155/2011/879716
Tesche, C.D., Uusitalo, M.A., Ilmoniemi, R.J., Huotilainen, M., Kajola, M., Salonen, O.: Signal-space projections of meg data characterize both distributed and well-localized neuronal sources. Electroencephalogr. Clin. Neurophysiol. 95, 189–200 (1995)
Tukey, J.W.: Exploratory Data Analysis. Addison-Wesley (1977)
Ungureanu, M., Bigan, C., Strungaru, R., Lazarescu, V.: Independent component analysis applied in biomedical signal processing. Measurement Sci. Rev. 4, 1–8 (2004)
Uusitalo, M.A., Ilmoniemi, R.J.: Signal-space projection method for separating meg or eeg into components. Med. Biol. Eng. Comput. 35, 135–140 (1997)
Velasco-Alvarez, F., Ron-Angevin, R., Lopez-Gordo, M.A.: Bci-based navigation in virtual and real environments. IWANN. LNCS 7903, 404–412 (2013)
Vidaurre, C., Blankertz, B.: Towards a cure for bci illiteracy. Brain Topogr. 23, 194–198 (2010)
Witten, I.H., Frank, E., Hall, M.A.: Data mining: Practical machine learning tools and techniques. In: Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann (2011). www.cs.waikato.ac.nz/ml/weka/, Accessed Mar 1st 2017
Wu, C.C., Hamm, J.P., Lim, V.K., Kirk, I.J.: Mu rhythm suppression demonstrates action representation in pianists during passive listening of piano melodies. Exp. Brain Res. 234, 2133–2139 (2016)
Xia, B., Li, X., Xie, H.: Asynchronous brain-computer interface based on steady-state visual-evoked potential. Cogn. Comput. 5(243) (2013)
Yang, J., Singh, H., Hines, E., Schlaghecken, F., Iliescu, D.: Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach. Artif. Intell. Med. 55, 117–126 (2012)
Yuan, H., He, B.: Brain-computer interfaces using sensorimotor rhythms: current state and future perspectives. IEEE Trans. Biomed. Eng. 61, 1425–1435 (2014)
Zhang, R., Xu, P., Guo, L., Zhang, Y., Li, P., Yao, D.: Z-score linear discriminant analysis for EEG based brain-computer interfaces. PLoS ONE. 8(9) (2013)
Acknowledgements
The research is funded by the National Science Centre of Poland on the basis of the decision DEC-2014/15/B/ST7/04724.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Szczuko, P., Lech, M., Czyżewski, A. (2018). Comparison of Classification Methods for EEG Signals of Real and Imaginary Motion. In: Stańczyk, U., Zielosko, B., Jain, L. (eds) Advances in Feature Selection for Data and Pattern Recognition. Intelligent Systems Reference Library, vol 138. Springer, Cham. https://doi.org/10.1007/978-3-319-67588-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-67588-6_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67587-9
Online ISBN: 978-3-319-67588-6
eBook Packages: EngineeringEngineering (R0)