Abstract
This paper proposes the use of two models of neural networks (Multi Layer Perceptron and Dendrite Morphological Neural Network) for the recognition of voluntary movements from electroencephalographic (EEG) signals. The proposal consisted of three main stages: organization of EEG signals, feature extraction and execution of classification algorithms. The EEG signals were recorded from eighteen healthy subjects performing self-paced reaching movements. Three classification scenarios were evaluated in each participant: Relax versus Intention, Relax versus Execution and Intention versus Execution. The feature extraction stage was carried out by applying an algorithm known as Common Spatial Pattern, in addition to the statistical methods called Root Mean Square, Variance, Standard Deviation and Mean. The results showed that the models of neural networks provided decoding accuracies above chance level, whereby, it is able to detect a movement prior its execution. On the basis of these results, the neural networks are a powerful promising classification technique that can be used to enhance performance in the recognition of motor tasks for BCI systems based on electroencephalographic signals.
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Guger, C., Allison, B.Z., Edlinger, G. (eds.): Brain-Computer Interface Research. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36083-1
Antelis, J.M., Gudiño-Mendoza, B., Falcón, L.E., Sanchez-Ante, G., Sossa, H.: Dendrite morphological neural networks for motor task recognition from electroencephalographic signals. Biomed. Signal Process. Control 44, 12–24 (2018). https://doi.org/10.1016/j.bspc.2018.03.010
Asensio Cubero, J., Gan, J.Q., Palaniappan, R.: Extracting optimal tempo-spatial features using local discriminant bases and common spatial patterns for brain computer interfacing. Biomed. Signal Process. Control 8(6), 772–778 (2013). https://doi.org/10.1016/j.bspc.2013.07.004
Bayliss, J.D.: Use of the evoked potential P3 component for control in a virtual apartment. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 113–116 (2003). https://doi.org/10.1109/TNSRE.2003.814438
Belhadj, S.A., Benmoussat, N., Krachai, M.D.: CSP features extraction and FLDA classification of EEG-based motor imagery for brain-computer interaction. In: 2015 4th International Conference on Electrical Engineering, ICEE 2015, pp. 3–8 (2016). https://doi.org/10.1109/INTEE.2015.7416697
Combrisson, E., Jerbi, K.: Exceeding chance level by chance: the caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy. J. Neurosci. Methods 250, 126–136 (2015). https://doi.org/10.1016/j.jneumeth.2015.01.010
Donchin, E., Spencer, K.M., Wijesinghe, R.: The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans. Rehabil. Eng. 8(2), 174–179 (2000). https://doi.org/10.1109/86.847808
Goutte, C., Gaussier, E.: A probabilistic interpretation of precision, recall and f-score, with implication for. Evaluation 3408, 345–359 (2005). https://doi.org/10.1007/978-3-540-31865-125
Gudiño-Mendoza, B., Sossa, H., Sanchez-Ante, G., Antelis, J.M.: Classification of motor states from brain rhythms using lattice neural networks. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Ayala-Ramírez, V., Olvera-López, J.A., Jiang, X. (eds.) MCPR 2016. LNCS, vol. 9703, pp. 303–312. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39393-3_30
Han, R.X., Wei, Q.G.: Feature extraction by combining wavelet packet transform and common spatial pattern in brain-computer interfaces. Appl. Mech. Mater. 239, 974–979 (2013). https://doi.org/10.4028/www.scientific.net/AMM.239-240.974
Hosni, S.M., Gadallah, M.E., Bahgat, S.F., AbdelWahab, M.S.: Classification of EEG signals using different feature extraction techniques for mental-taskBCI. In: 2007 International Conference on Computer Engineering Systems, pp. 220–226 (2007). https://doi.org/10.1109/ICCES.2007.4447052
Iturrate, I., Antelis, J.M., Andrea, K., Minguez, J.: A noninvasive brain-actuated wheelchair based on a P300 neurophysiological protocol and automated navigation. IEEE Trans. Robot. 25(3), 614–627 (2009)
Katona, J., Kovari, A.: EEG-based computer control interface for brain-machine interaction. Int. J. Online Eng. 11(6), 43–48 (2015). https://doi.org/10.3991/ijoe.v11i6.5119
Li, M., Li, W., Zhao, J., Meng, Q., Zeng, M., Chen, G.: A P300 model for cerebot – a mind-controlled humanoid robot. In: Kim, J.-H., Matson, E.T., Myung, H., Xu, P., Karray, F. (eds.) Robot Intelligence Technology and Applications 2. AISC, vol. 274, pp. 495–502. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05582-4_43
Li, Y., et al.: An EEG-based BCI system for 2-D cursor control by combining Mu/Beta rhythm and P300 potential. IEEE Trans. Biomed. Eng. 57(10 PART 1), 2495–2505 (2010). https://doi.org/10.1109/TBME.2010.2055564
Ma, Y., Ding, X., She, Q., Luo, Z., Potter, T., Zhang, Y.: Classification of motor imagery EEG signals with support vector machines and particle swarm optimization. Comput. Math. Methods Med. 2016(5), 667–677 (2016). https://doi.org/10.1155/2016/4941235
Purves, D., et al.: Neuroscience, vol. 3 (2004). ISBN 978-0878937257
Ritter, G.X., Sussner, P.: An introduction to morphological neural networks. In: Proceedings - International Conference on Pattern Recognition, vol. 4, pp. 709–717 (1996). https://doi.org/10.1109/ICPR.1996.547657
Sossa, H., Guevara, E.: Efficient training for dendrite morphological neural networks. Neurocomputing 131, 132–142 (2014). https://doi.org/10.1016/j.neucom.2013.10.031
Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113, 767–791 (2002)
Wolpaw, J.R., McFarland, D.J., Neat, G.W., Forneris, C.A.: An EEG-based brain-computer interface for cursor control. Electroencephalogr. Clin. Neurophysiol. 78(3), 252–259 (1991). https://doi.org/10.1016/0013-4694(91)90040-B, http://www.sciencedirect.com/science/article/pii/001346949190040B
Zhang, Y., Zhou, G., Jin, J., Wang, X., Cichocki, A.: Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface. J. Neurosci. Methods 255, 85–91 (2015). https://doi.org/10.1016/j.jneumeth.2015.08.004
Acknowledgements
We would like to express our sincere appreciation to the Instituto Politécnico Nacional and the Secretaria de Investigación y Posgrado for the economic support provided to carry out this research. This project was supported economically by SIP-IPN (numbers 20180730 and 20180943) and the National Council of Science and Technology of Mexico (CONACyT) (65 Frontiers of Science, numbers 268958 and PN2015-873).
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Virgilio Gonzalez, C.D., Sossa Azuela, J.H., Antelis, J.M. (2018). Artificial Neural Networks and Common Spatial Patterns for the Recognition of Motor Information from EEG Signals. In: Batyrshin, I., Martínez-Villaseñor, M., Ponce Espinosa, H. (eds) Advances in Soft Computing. MICAI 2018. Lecture Notes in Computer Science(), vol 11288. Springer, Cham. https://doi.org/10.1007/978-3-030-04491-6_9
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