Abstract
The paper presents application of neural networks to the construction of a brain-computer interface (BCI) based on the Motor Imagery paradigm. The BCI was constructed for ten electroencephalographic (EEG) signals collected and analysed in real time.The filtered signals were divided into three groups corresponding to the information displayed to users on the screen during the experiments. ANOVA analysis and automatic construction of a neural network (NN) classification were also performed. Results of the ANOVA analysis were confirmed by the neural networks efficiency analysis. The efficiency of NN classification of the left and right hemisphere activities reached almost 70 %.
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An, X., Kuang, D., Guo, X., Zhao, Y., He, L.: A deep learning method for classification of EEG data based on motor imagery. In: Huang, D.-S., Han, K., Gromiha, M. (eds.) ICIC 2014. LNCS, vol. 8590, pp. 203–210. Springer, Heidelberg (2014)
Barbati, G., Porcaro, C., Zappasodi, F., Rossini, P., Tecchio, F.: Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals. Clin. Neurophysiol. 115, 1220–1232 (2004)
Blinowska, K., Kaminski, M.: Multivariate signal analysis by parametric models. In: Schelter, B., Winterhalder, M., Timmer, J. (eds.) Handbook of Time SeriesAnalysis. WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim (2006)
Bromfield, E., Cavazos, J., Sirven, J.: Basic mechanisms underlying seizures and epilepsy. In: An Introduction to Epilepsy (2006)
Broniec-Wojcik, A.: Ph.D. dissertation. AGH, Krakow (2013)
Cho, B., Lee, J., Ku, J., Jang, D., Kim, J., Kim, I., Kim, S.: Attention enhancement system using virtual reality and EEG biofeedback. In: Virtual Reality, Proceedings, IEEE, pp. 156–163 (2002)
Croft, R., Barry, R.: Eog correction: a new perspective. Electroencephalogr. Clin. Neurophysiol. 107, 387–394 (1998)
Croft, R., Barry, R.: Removal of ocular artifact from the EEG: a review. Neuro. Physiol. Clin. 30, 5–19 (2000)
Diab, M., Ismail, G., Al-Jawha, M., Hsaiky, A., Moslem, B., Sabbah, M., Taha, M.: Biofeedback for epilepsy treatment. In: Mechatronics and its Applications (ISMA), pp. 1–4. IEEE (2012)
Geethanjali, P., Mohan, Y., Sen, J.: Time domain feature extraction and classification of eeg data for brain computer interface. In: 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE (2012)
Joyce, C., Gorodnitsky, I., Kutas, M.: Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. Psychophysiol. 41, 313–325 (2004)
Jung, T., Makeig, S., Humphries, C., Lee, T.W., Mckeown, M.J., Iragui, V., Sejnowski, T.J.: Removingelectroencephalographic artifacts by blind source separation. Psychophysiol. 37, 163–178 (2000)
al-Ketbi, O., Conrad, M.: Supervised ANN vs. unsupervised SOM to classify EEG data for BCI: Why can GMDH do better? Int. J. Comput. Appl. 74(4), 37–44 (2013)
Kornhuber, H.H., Deecke, L.: Changes in human brain potentials before and after voluntary movement studied by recording on magnetic tape and reverse analysis (1964)
Koronacki, J., Cwik, J.: Statisticcal learning systems (in Polish: Statystyczne systemy uczace sie), Exit (2008)
Lee, S., Abibullaev, B., Kang, W., Shin, Y., An, J.: Analysis of attention deficit hyperactivity disorder in EEG using wavelet transform and self organizing maps. In: Control Automation and Systems (ICCAS), pp. 2439–2442 (2010)
Mingyu, L., Jue, W., Nan, Y., Qin, Y.: Development of EEG biofeedback system based on virtual reality environment. In: Engineering in Medicine and Biology Society, pp. 5362–5364 (2005)
Neuper, C., Miller, G., Kebler, A., Birbaumer, N., Pfurtscheller, G.: Clinical application of an eeg-based brain-computer interface: a case study in a patient with severe motor impairment. Clin. Neurophysiol. 114(3), 399–409 (2003)
Nowak-Brzezińska, A., Jach, T.: The incompleteness factor method as a support of inference in decision support systems. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B. (eds.) BDAS 2014. CCIS, vol. 424, pp. 201–210. Springer, Heidelberg (2014)
Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. Proc. IEEE 89(7), 1123–1134 (2001)
Pfurtscheller, G., da Silva, L.: Functional Meaning of Event-Related Desynchronization (ERD) and Synchronization (ERS), pp. 51–65 (1999)
Shim, B., Lee, S.W., Shin., J.H.: Implementation of a 3-dimensional game fordeveloping balanced brainwave. In: Software Engineering Research, Management & Applications, SERA 2007 (2007)
Suresh, K., Heng, J.: Quantitative eeg parameters for monitoring and biofeedback during rehabilitation after stroke. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics (2009)
Van Vliet, M., Robben, A., Chumerin, N., Manyakov, N., Combaz, A., Van Hulle, M.: Designing a brain-computer interface controlled video-game using consumer grade EEG hardware. In: Biosignals and Biorobotics Conference (BRC 2012), pp. 1–6. ISSNIP (2012)
Vidal, J.: Toward direct brain-computer communication. Ann. Rev. Biophys. Bioeng. 2(1), 157–180 (1973)
Wolpaw, J., Birbaumer, N., Heetderks, W., McFarland, D., Peckham, P., Schalk, G., Vaughan, T.: Brain-computer interface technology: a review of the firstinternational meeting. IEEE Trans. Rehabil. Eng. 8(2), 164–173 (2000)
Żbikowski, K.: Time series forecasting with volume weighted support vector machines. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B. (eds.) BDAS 2014. CCIS, vol. 424, pp. 250–258. Springer, Heidelberg (2014)
Zielosko, B.: Optimization of inhibitory decision rules relative to coverage - comparative studys. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2014. CCIS, vol. 521, pp. 267–276. Springer, Heidelberg (2014)
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Plechawska-Wojcik, M., Wolszczak, P. (2016). Appling of Neural Networks to Classification of Brain-Computer Interface Data. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery. BDAS BDAS 2015 2016. Communications in Computer and Information Science, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-34099-9_37
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