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2019 | OriginalPaper | Buchkapitel

Comparison of Methods for Real and Imaginary Motion Classification from EEG Signals

verfasst von : Piotr Szczuko, Michał Lech, Andrzej Czyżewski

Erschienen in: Intelligent Methods and Big Data in Industrial Applications

Verlag: Springer International Publishing

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Abstract

A method for feature extraction and results of classification of EEG signals obtained from performed and imagined motion are presented. A set of 615 features was obtained to serve for the recognition of type and laterality of motion using 8 different classifications approaches. A comparison of achieved classifiers accuracy is presented in the paper, and then conclusions and discussion are provided. Among applied algorithms the highest accuracy was achieved with: Rough Set, SVM and ANN methods.

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Literatur
7.
Zurück zum Zitat 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., Pfurtscheller, G.: Walking through a virtual city by thought. In: Proceedings of the 26th Annual International Conference of the IEEE EMBS (2004)
8.
Zurück zum Zitat 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) 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)
9.
Zurück zum Zitat 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)CrossRef 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)CrossRef
11.
Zurück zum Zitat Velasco-Alvarez, F., Ron-Angevin, R., Lopez-Gordo, M.A.: BCI-based navigation in virtual and real environments. IWANN. LNCS 7903, 404–412 (2013) Velasco-Alvarez, F., Ron-Angevin, R., Lopez-Gordo, M.A.: BCI-based navigation in virtual and real environments. IWANN. LNCS 7903, 404–412 (2013)
13.
16.
Zurück zum Zitat Iscan, Z.: Detection of P300 wave from EEG data for brain-computer interface applications. Pattern Recognit. Image Anal. 21, 481 (2011)CrossRef Iscan, Z.: Detection of P300 wave from EEG data for brain-computer interface applications. Pattern Recognit. Image Anal. 21, 481 (2011)CrossRef
21.
Zurück zum Zitat Hajibabazadeh, M., Azimirad, V.: Brain-robot interface: distinguishing left and right hand EEG signals through SVM. In: Proceedings of the 2nd RSI/ISM International Conference on Robotics and Mechatronics, Tehran, Iran, 15–17 October 2014 Hajibabazadeh, M., Azimirad, V.: Brain-robot interface: distinguishing left and right hand EEG signals through SVM. In: Proceedings of the 2nd RSI/ISM International Conference on Robotics and Mechatronics, Tehran, Iran, 15–17 October 2014
22.
Zurück zum Zitat Sun, H., Xiang, Y., Sun, Y., Zhu, H., Zeng, J.: On-line EEG classification for brain-computer interface based on CSP and SVM. In: 3rd International Congress on Image and Signal Processing (2010) Sun, H., Xiang, Y., Sun, Y., Zhu, H., Zeng, J.: On-line EEG classification for brain-computer interface based on CSP and SVM. In: 3rd International Congress on Image and Signal Processing (2010)
23.
Zurück zum Zitat Sonkin, K., Stankevich, L., Khomenko, J., Nagornova, Z., Shemyakina, N.: Development of electroencephalographic pattern classifiers for real and imaginary thumb and index finger movements of one hand. Artif. Intell. Med. 63, 107–117 (2015)CrossRef Sonkin, K., Stankevich, L., Khomenko, J., Nagornova, Z., Shemyakina, N.: Development of electroencephalographic pattern classifiers for real and imaginary thumb and index finger movements of one hand. Artif. Intell. Med. 63, 107–117 (2015)CrossRef
24.
Zurück zum Zitat 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)CrossRef 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)CrossRef
25.
Zurück zum Zitat Schwarz, A., Scherer, R., Steyrl, D., Faller, J., Müller-Putz, G.: Co-adaptive sensory motor rhythms brain-computer interface based on common spatial patterns and random forest. In: 37th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC) (2015) Schwarz, A., Scherer, R., Steyrl, D., Faller, J., Müller-Putz, G.: Co-adaptive sensory motor rhythms brain-computer interface based on common spatial patterns and random forest. In: 37th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC) (2015)
26.
Zurück zum Zitat Siuly, S., Wang, H., Zhang, Y.: Detection of motor imagery EEG signals employing Naïve Bayes based learning process. J. Meas 86, 148–158 (2016)CrossRef Siuly, S., Wang, H., Zhang, Y.: Detection of motor imagery EEG signals employing Naïve Bayes based learning process. J. Meas 86, 148–158 (2016)CrossRef
27.
Zurück zum Zitat 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)CrossRef 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)CrossRef
28.
Zurück zum Zitat 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, e74433 (2013) 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, e74433 (2013)
36.
Zurück zum Zitat 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)CrossRef 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)CrossRef
37.
Zurück zum Zitat 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)CrossRef 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)CrossRef
41.
Zurück zum Zitat 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)CrossRef 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)CrossRef
42.
Zurück zum Zitat Ungureanu, M., Bigan, C., Strungaru, R., Lazarescu, V.: Independent component analysis applied in biomedical signal processing. Meas. Sci. Rev. 4, 1–8 (2004) Ungureanu, M., Bigan, C., Strungaru, R., Lazarescu, V.: Independent component analysis applied in biomedical signal processing. Meas. Sci. Rev. 4, 1–8 (2004)
44.
Zurück zum Zitat 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). [ocirc.ahajournals.org/cgi/content/full/101/23/e215c]; physionet.org/pn4/eegmmidb. Accessed 2 Feb 2017CrossRef 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). [ocirc.ahajournals.org/cgi/content/full/101/23/e215c]; physionet.org/pn4/eegmmidb. Accessed 2 Feb 2017CrossRef
46.
Zurück zum Zitat Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51, 1034–1043 (2004)CrossRef Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51, 1034–1043 (2004)CrossRef
47.
Zurück zum Zitat Marple, S.L.: Computing the discrete-time analytic signal via FFT. IEEE Trans. Signal Proc. 47, 2600–2603 (1999)CrossRef Marple, S.L.: Computing the discrete-time analytic signal via FFT. IEEE Trans. Signal Proc. 47, 2600–2603 (1999)CrossRef
50.
Zurück zum Zitat Riza, S.L., Janusz, A., Ślęzak, 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 2 Feb 2017 Riza, S.L., Janusz, A., Ślęzak, 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 2 Feb 2017
52.
Zurück zum Zitat John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: 11th Conference on Uncertainty in Artificial Intelligence, San Mateo, pp. 338–345 (1995) John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: 11th Conference on Uncertainty in Artificial Intelligence, San Mateo, pp. 338–345 (1995)
53.
Zurück zum Zitat Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Schoelkopf, B., et al. (eds.) Advances in Kernel Methods—Support Vector Learning (1998) Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Schoelkopf, B., et al. (eds.) Advances in Kernel Methods—Support Vector Learning (1998)
54.
Zurück zum Zitat Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murthy, K.R.K.: Improvements to Platt’s SMO algorithm for SVM classifier design. Neural Comput. 13(3), 637–649 (2001)CrossRef Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murthy, K.R.K.: Improvements to Platt’s SMO algorithm for SVM classifier design. Neural Comput. 13(3), 637–649 (2001)CrossRef
55.
Zurück zum Zitat Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann (1993) Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann (1993)
56.
Zurück zum Zitat Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, pp. 148–156 (1996) Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, pp. 148–156 (1996)
57.
Zurück zum Zitat Martin, B.: Instance-Based Learning: Nearest Neighbour with Generalization. Hamilton, New Zealand (1995) Martin, B.: Instance-Based Learning: Nearest Neighbour with Generalization. Hamilton, New Zealand (1995)
58.
Zurück zum Zitat Roy, S.: Nearest Neighbor with Generalization. Christchurch, New Zealand (2002) Roy, S.: Nearest Neighbor with Generalization. Christchurch, New Zealand (2002)
59.
Zurück zum Zitat Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)CrossRef Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)CrossRef
60.
Zurück zum Zitat Janusz, A., Stawicki, S.: Applications of approximate reducts to the feature selection problem. Proc. Int. Conf. Rough Sets Knowl. Technol. (RSKT) 6954, 45–50 (2011)CrossRef Janusz, A., Stawicki, S.: Applications of approximate reducts to the feature selection problem. Proc. Int. Conf. Rough Sets Knowl. Technol. (RSKT) 6954, 45–50 (2011)CrossRef
61.
Zurück zum Zitat Szczuko, P.: Rough set-based classification of EEG signals related to real and imagery motion. In: Proceedings of the Signal Processing Algorithms, Architectures, Arrangements, and Applications, Poznań (2016) Szczuko, P.: Rough set-based classification of EEG signals related to real and imagery motion. In: Proceedings of the Signal Processing Algorithms, Architectures, Arrangements, and Applications, Poznań (2016)
62.
Zurück zum Zitat Szczuko, P.: Real and Imagery Motion Classification Based on Rough Set Analysis of EEG Signals for Multimedia Applications. Multimedia Tools and Applications (2017) Szczuko, P.: Real and Imagery Motion Classification Based on Rough Set Analysis of EEG Signals for Multimedia Applications. Multimedia Tools and Applications (2017)
63.
Zurück zum Zitat Tukey, J.W.: Exploratory Data Analysis. Addison-Wesley (1977) Tukey, J.W.: Exploratory Data Analysis. Addison-Wesley (1977)
Metadaten
Titel
Comparison of Methods for Real and Imaginary Motion Classification from EEG Signals
verfasst von
Piotr Szczuko
Michał Lech
Andrzej Czyżewski
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-319-77604-0_18