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2019 | OriginalPaper | Chapter

Clusterized KNN for EEG Channel Selection and Prototyping of Lower Limb Joint Torques

Authors : Lucero Alvarado, Griselda Quiroz, Angel Rodriguez-Liñan, Luis Torres-Treviño

Published in: Advances in Soft Computing

Publisher: Springer International Publishing

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Abstract

In this paper, a method for automatic channel selection of EEG signals acquired during the execution of lower limb movements is presented; for this method the hip and knee joint torques are measured. The method is based on maximizing both the percentage of prototypes extracted and the relative dispersion of its respective torques using a genetic algorithm. The prototyping is made with clusterized KNN, a proposed modification of the K-nearest neighbors algorithm, and the dispersion is computed as the ratio of interquartile ranges (IQR) between original and resulting torques. Results show that frequent channels are consistent with those known to be activated during motor tasks and that additional channels, needed for extracting relevant information from the data, vary from subject to subject. Extracted data can be used as new inputs for later regression tasks and for further analysis in order to characterize neural processes.

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Aggarwal, C.C., Hinneburg, A., Keim, D.A.: On the surprising behavior of distance metrics in high dimensional space. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 420–434. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44503-X_27CrossRef

Ball, T., Kern, M., Mutschler, I., Aertsen, A., Schulze-Bonhage, A.: Signal quality of simultaneously recorded invasive and non-invasive EEG. NeuroImage 46(3), 708–716 (2009). https://doi.org/10.1016/j.neuroimage.2009.02.028CrossRef

Centers for Disease Control and Prevention (CDC): Disability and functioning (noninstitutionalized adults aged 18 and over) (2016). Accessed May 2019

Department of Social Development (SEDESOL): Diagnosis on the situation of people with disabilities in Mexico (2016), Accessed May 2019

Ditterich, J.: Evidence for time-variant decision making. Eur. J. Neurosci. 24(12), 3628–3641 (2006). https://doi.org/10.1111/j.1460-9568.2006.05221.xCrossRef

Druckmann, S., Chklovskii, D.B.: Neuronal circuits underlying persistent representations despite time varying activity. Current Biol. 22(22), 2095–2103 (2012). https://doi.org/10.1016/j.cub.2012.08.058CrossRef

Duun-Henriksen, J., Kjaer, T.W., Madsen, R.E., Remvig, L.S., Thomsen, C.E., Sorensen, H.B.D.: Channel selection for automatic seizure detection. Clin. Neurophysiol. 123(1), 84–92 (2012). https://doi.org/10.1016/j.clinph.2011.06.001CrossRef

Ferree, T.C., Luu, P., Russell, G.S., Tucker, D.M.: Scalp electrode impedance, infection risk, and EEG data quality. Clin. Neurophysiol. 112(3), 536–544 (2001). https://doi.org/10.1016/s1388-2457(00)00533-2CrossRef

Gonzalez, A., Nambu, I., Hokari, H., Wada, Y.: EEG channel selection using particle swarm optimization for the classification of auditory event-related potentials. Sci. World J. 2014, 1–11 (2014). https://doi.org/10.1155/2014/350270CrossRef

10.

Hechenbichler, K., Schliep, K.: Weighted k-nearest-neighbor techniques and ordinal classification (2004). https://doi.org/10.5282/ubm/epub.1769

11.

van den Heuvel, M.P., Pol, H.E.H.: Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur. Neuropsychopharmacol. 20(8), 519–534 (2010). https://doi.org/10.1016/j.euroneuro.2010.03.008CrossRef

12.

Holland, J.: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Adaptation in Natural and Artificial Systems, 1st edn. The University of Michigan, Ann Arbor (1975)MATH

13.

Jahanshahi, M., Hallett, M.: The Bereitschaftspotential: Movement-Related Cortical Potentials. Springer, New York (2003). https://doi.org/10.1007/978-1-4615-0189-3CrossRef

14.

Jorge, J., Grouiller, F., Gruetter, R., van der Zwaag, W., Figueiredo, P.: Towards high-quality simultaneous EEG-fMRI at 7T: detection and reduction of EEG artifacts due to head motion. NeuroImage 120, 143–153 (2015). https://doi.org/10.1016/j.neuroimage.2015.07.020CrossRef

15.

Kelleher, J.D., Mac Namee, B., D’arcy, A.: Fundamentals of Machine Learning for Predictive Data Analytics Algorithms Worked Examples and Case Studies. MIT Press, Cambridge (2015)MATH

16.

Kumar, T.: Solution of linear and non linear regression problem by k nearest neighbour approach: by using three sigma rule. In: 2015 IEEE International Conference on Computational Intelligence & Communication Technology. IEEE (2015). https://doi.org/10.1109/cict.2015.110

17.

Lal, T., et al.: Support vector channel selection in BCI. IEEE Trans. Biomed. Eng. 51(6), 1003–1010 (2004). https://doi.org/10.1109/tbme.2004.827827CrossRef

18.

Leistritz, L., Schiecke, K., Astolfi, L., Witte, H.: Time-variant modeling of brain processes. Proc. IEEE 104(2), 262–281 (2016). https://doi.org/10.1109/jproc.2015.2497144CrossRef

19.

Nakagome, S., Luu, T.P., Brantley, J.A., Contreras-Vidal, J.L.: Prediction of EMG envelopes of multiple terrains over-ground walking from EEG signals using an unscented Kalman filter. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, October 2017. https://doi.org/10.1109/smc.2017.8123116

20.

Pedregosa, F., et al.: Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011). http://dl.acm.org/citation.cfm?id=1953048.2078195MathSciNetMATH

21.

Presacco, A., Goodman, R., Forrester, L., Contreras-Vidal, J.L.: Neural decoding of treadmill walking from noninvasive electroencephalographic signals. J. Neurophysiol. 106(4), 1875–1887 (2011). https://doi.org/10.1152/jn.00104.2011CrossRef

22.

Raichle, M.E.: The restless brain. Brain Connect. 1(1), 3–12 (2011). https://doi.org/10.1089/brain.2011.0019CrossRef

23.

Ramos-Murguialday, A., Birbaumer, N.: Brain oscillatory signatures of motor tasks. J. Neurophysiol. 113(10), 3663–3682 (2015). https://doi.org/10.1152/jn.00467.2013CrossRef

24.

Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition, pp. 159–165 (1990). https://doi.org/10.1016/b978-0-08-051584-7.50016-4CrossRef

25.

Vázquez, R.R., Vélez-Pérez, H., Ranta, R., Dorr, V.L., Maquin, D., Maillard, L.: Blind source separation, wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling. Biomed. Signal Process. Control 7(4), 389–400 (2012). https://doi.org/10.1016/j.bspc.2011.06.005CrossRef

26.

Zhang, Y., Prasad, S., Kilicarslan, A., Contreras-Vidal, J.L.: Multiple kernel based region importance learning for neural classification of gait states from EEG signals. Front. Neurosci. 11, 170 (2017). https://doi.org/10.3389/fnins.2017.00170CrossRef

27.

Zwillinger, D., Kokoska, S.: CRC Standard Probability and Statistics Tables and Formulae. CRC Press, Boca Raton (1999)CrossRef

Title: Clusterized KNN for EEG Channel Selection and Prototyping of Lower Limb Joint Torques
Authors: Lucero Alvarado
Griselda Quiroz
Angel Rodriguez-Liñan
Luis Torres-Treviño
Publisher: Springer International Publishing
Book: Advances in Soft Computing
Print ISBN: 978-3-030-33748-3

Electronic ISBN: 978-3-030-33749-0

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-33749-0_50

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