Abstract
Motor Imagery is widely used applications, for which the analysis of underlying mechanisms is mostly focused on classification tasks. Relying on the Fractional Permutation Entropy (FPE), we propose a complexity-based analysis of individual brain dynamics extracted from motor imagery tasks. Due to conventional Common Spatial Pattern (CSP) filtering is affected by the high variability across trials, we perform the study of different stages of feature extraction, including raw data representation, conventional CSP, and high-level dynamics of the filter-banked CSP feature sets. Obtained results on a real-world application prove that promote the higher separation between patients extracting higher-level dynamics from features.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alvarez-Meza, A., Velasquez-Martinez, L., et al.: Time-series discrimination using feature relevance analysis in motor imagery classification. Neurocomputing 151, 122–129 (2015)
Ang, K., Chin, Z., et al.: Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front. Neurosci. 6, 39 (2012)
Blankertz, B., Tomioka, R., et al.: Optimizing spatial filters for robust eeg single-trial analysis. IEEE Signal Process. Mag. 25(1), 41–56 (2007)
Caicedo-Acosta, J., Cárdenas-Peña, D., Collazos-Huertas, D., Padilla-Buritica, J.I., Castaño-Duque, G., Castellanos-Dominguez, G.: Multiple-instance lasso regularization via embedded instance selection for emotion recognition. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds.) IWINAC 2019. LNCS, vol. 11486, pp. 244–251. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-19591-5_25
Chen, Y., Bi, J., et al.: MILES: multiple-instance learning via embedded instance selection. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 1931–1947 (2006)
Chu, C., Wang, J., et al.: Complexity analysis of EEG in AD patients with fractional permutation entropy. In: 2018 37th Chinese Control Conference, pp. 4346–4350 (2018)
Frau-Meigs, D.: Media Education. Parents and Professionals. Unesco, A Kit for Teachers, Students (2007)
Hsu, W.-Y.: Assembling a multi-feature EEG classifier for left-right motor imagery data using wavelet-based fuzzy approximate entropy for improved accuracy. Int. J. Neural syst. 25(8), 1550037 (2015)
Liu, Y., Huang, S., et al.: Novel motor imagery-based brain switch for patients with amyotrophic lateral sclerosis: a case study using two-channel electroencephalography. IEEE Consum. Electron. Mag. 8(2), 72–77 (2019)
Miao, M., Wang, A., et al.: A spatial-frequency-temporal optimized feature sparse representation-based classification method for motor imagery EEG pattern recognition. Med. Biol. Eng. Comput. 55(9), 1589–1603 (2017)
Passalis, N., Tsantekidis, A., et al.: Time-series classification using neural Bag-of-Features. In: 2017 25th European Signal Processing Conference (EUSIPCO), pp. 301–305 (2017)
Shin, Y., Lee, S., et al.: Sparse representation-based classification scheme for motor imagery-based brain-computer interface systems. J. Neural Eng. 9(5), 056002 (2012)
Zanin, M., Gómez-Andrés, D., et al.: Characterizing normal and pathological gait through permutation entropy. Entropy 20(1), 77 (2018)
Zhang, Y., Zhou, G., et al.: Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface. J. Neurosci. Methods 255, 85–91 (2015)
Acknowledgments
This research was supported by Doctorados Nacionales, conv.727 funded by COLCIENCIAS, and the research project “Programa reconstrucción del tejido social en zonas de pos-conflicto en Colombia del proyecto Fortalecimiento docente desde la alfabetización mediática Informacional y la CTel, como estrategia didáctico-pedagógica y soporte para la recuperación de la confianza del tejido social afectado por el conflicto, Código SIGP 58950” funded by “Fondo Nacional de Financiamiento para la Ciencia, la Tecnología y la Innovación, Fondo Francisco José de Caldas con contrato No. \(213-2018\) con Código 58960”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Velasquez-Martinez, L.F., Arteaga, F., Castellanos-Dominguez, G. (2019). Subject-Oriented Dynamic Characterization of Motor Imagery Tasks Using Complexity Analysis. In: Liang, P., Goel, V., Shan, C. (eds) Brain Informatics. BI 2019. Lecture Notes in Computer Science(), vol 11976. Springer, Cham. https://doi.org/10.1007/978-3-030-37078-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-37078-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37077-0
Online ISBN: 978-3-030-37078-7
eBook Packages: Computer ScienceComputer Science (R0)