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.
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- Accepted or Published Version
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- Category:
- Monographic publication
- Type:
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Title of issue:
- Advances in Feature Selection for Data and Pattern Recognition strony 227 - 239
- Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- Szczuko P., Lech M., Czyżewski A.: Comparison of Classification Methods for EEG Signals of Real and Imaginary Motion// Advances in Feature Selection for Data and Pattern Recognition/ ed. Stańczyk U., Zielosko B., Jain L. : Springer, 2018, s.227-239
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-319-67588-6_12
- Bibliography: test
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- 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) open in new tab
- BCI2000. Bci2000 instrumentation system project. http://www.bci2000.org, Accessed on 2017-03-01 open in new tab
- 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) open in new tab
- 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) open in new tab
- 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) open in new tab
- Choi, K.: Electroencephalography (eeg)-based neurofeedback training for brain-computer in- terface (bci). Exp. Brain Res. 231, 351-365 (2013) open in new tab
- 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) open in new tab
- Czyżewski, A., Kostek, B., Kurowski, A., Szczuko, P., Lech, M., Odya, P., Kwiatkowska, A.: Assessment of hearing in coma patients employing auditory brainstem response, electroen- cephalography and eye-gaze-tracking. In: Proceedings of the 173rd Meeting of the Acoustical Society of America (2017) open in new tab
- Dickhaus, T., Sannelli, C., Muller, K.R., Curio, G., Blankertz, B.: Predicting bci performance to study bci illiteracy. BMC Neurosci. 10 (2009) open in new tab
- Diez, P.F., Mut, V.A., Avila Perona, E.M.: Asynchronous bci control using high-frequency. SSVEP. J. NeuroEngineering. Rehabil. 8(39) (2011) open in new tab
- 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) open in new tab
- 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) open in new tab
- Gardener, M., Beginning, R.: The statistical programming language, (2012). https://cran.r- project.org/manuals.html, Accessed on 2017-03-01 open in new tab
- 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: com- ponents of a new research resource for complex physiologic signals. Circulation 101, 215-220 (2000) open in new tab
- 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 open in new tab
- Iscan, Z.: Detection of p300 wave from eeg data for brain-computer interface applications. Pattern Recognit. Image Anal. 21(481) (2011) open in new tab
- 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) open in new tab
- John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: Pro- ceedings of the 11th Conference on Uncertainty in Artificial Intelligence, pp. 338-345 (1995) open in new tab
- 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) open in new tab
- 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) open in new tab
- 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) open in new tab
- 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) open in new tab
- 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) open in new tab
- Leeb, R., Pfurtscheller, G.: Walking through a virtual city by thought. In: Proceedings of the 26th Annual International Conference of the IEEE EMBS, (2004) open in new tab
- 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) open in new tab
- 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) open in new tab
- Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341-356 (1982) open in new tab
- Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. Proc. of IEEE 89, 1123-1134 (2001) open in new tab
- 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) open in new tab
- Postelnicu, C., Talaba, D.: P300-based brain-neuronal computer interaction for spelling appli- cations. IEEE Trans. Biomed. Eng. 60, 534-543 (2013) open in new tab
- 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., Staw- icki, S.; Roughsets: data analysis using rough set and fuzzy rough set theories, (2015). https:// github.com/janusza/RoughSets, Accessed on 2017-03-01 open in new tab
- Roy, S.: Nearest neighbor with generalization. Christchurch, New Zealand (2002) open in new tab
- 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) open in new tab
- 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) open in new tab
- 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) open in new tab
- Silva, J., Torres-Solis, J., Chau, T.: A novel asynchronous access method with binary interfaces. J. NeuroEng. Rehabil. 5(24) (2008) open in new tab
- 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) open in new tab
- Suh, D., Sang Cho, H., Goo, J., Park, K.S., Hahn, M.: Virtual navigation system for the dis- abled 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 open in new tab
- Szczuko, P., Lech, M., Czyżewski, A.: Comparison of methods for real and imaginary motion classification from eeg signals. In: Proceedings of ISMIS conference, (2017) open in new tab
- 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 open in new tab
- 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) open in new tab
- 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 open in new tab
- 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) open in new tab
- 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) open in new tab
- 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) open in new tab
- Velasco-Alvarez, F., Ron-Angevin, R., Lopez-Gordo, M.A.: Bci-based navigation in virtual and real environments. IWANN. LNCS 7903, 404-412 (2013) open in new tab
- Vidaurre, C., Blankertz, B.: Towards a cure for bci illiteracy. Brain Topogr. 23, 194-198 (2010) open in new tab
- Witten, I.H., Frank, E., Hall, M.A.: Data mining: Practical machine learning tools and tech- niques. In: Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann (2011). www.cs.waikato.ac.nz/ml/weka/, Accessed Mar 1st 2017 open in new tab
- 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) open in new tab
- Xia, B., Li, X., Xie, H.: Asynchronous brain-computer interface based on steady-state visual- evoked potential. Cogn. Comput. 5(243) (2013) open in new tab
- Yang, J., Singh, H., Hines, E., Schlaghecken, F., Iliescu, D.: Channel selection and classifica- tion of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach. Artif. Intell. Med. 55, 117-126 (2012) open in new tab
- 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) open in new tab
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