Characterization of motor imagination using decomposition analysis of energy bands
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Sent:
Aug 24, 2018
Published: Jan 31, 2019
Published: Jan 31, 2019
Abstract
This article provides an overview of the analysis of electrical signals generated by the brain, when a person imagines the
movement of their upper extremities. The signals studied are acquired with a brain-computer interface equipment and analyzed using Discrete
Wavelet transform as the energy band decomposition technique. The use of this technique is new in the analysis of electrical signals generated by
the brain and the results obtained indicate that the use of the technique is feasible for the characterization of electrical signals coming from the
brain.
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How to Cite
Moreno, I., Boya, C., Serracín, J., Serracín, S., Batista, E., Antelis, J., Hernández, L., Moreno, R., Gómez, L., & Quintero, J. (2019). Characterization of motor imagination using decomposition analysis of energy bands. I+D Tecnológico, 15(1), 71-79. https://doi.org/10.33412/idt.v15.1.2101
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(7) J. I. Serrano, M. D. D. Castillo, R. Raya, C. Bayón and E. Rocon, "BCI basado en la facilitación asociativa de la actividad cortical para el inicio de la marcha en Parálisis Cerebral," in 7º Simposio CEA de Bioingeniería 2015, Interfaces Cerebro-computador (BCI) y Tecnologías Asistenciales, Málaga, 2015.
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(13) E. F. Khalaf, K. Daqrouq and M. Sherif, "Wavelet Packet and Percent of Energy Distribution with Neural Networks Based Gender Identification System," Journal of Applied Sciences, pp. 2940-2946, 2011.
(14) I. Omerhodzic, S. Avdakovic, A. Nuhanovic and K. Dizdarevic, "Energy Distribution of EEG Signals: EEG Signal Wavelet-Neural Network Classifier". Caracterización de imaginación motora utilizando análisis de descomposición de bandas de energíaCharacterization of motor imagination using decomposition analysis of energy bands78RIDTEC | Vol. 15, n.° 1, enero - junio 2019.
(15) C. Boya, M. Ruiz-Llata, J. Posada and J. A. Garcia-Souto, "Identification of Multiple Partial Discharge Sources using Acoustic Emission Technique and Blind Source Separation," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 22, pp. 1663-1673.
(16) MathWorks, "Análisis Wavelet de 1-D MultiSignal," [Online]. Available:.https://www.mathworks.com/help/wavelet/examples/multisignal-1-d-wavelet-analysis.html [Accessed 25 Julio 2018].
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(18) A. Nijholt, R. J. Jacob, M. Andujar, B. F. Yuksel and G. Leslie, "Brain-Computer Interface for Artistic Expression," 2018.
(19) I. Kosunen, A. Ruonala, M. Salminen, S. Järvelä, N. Ravaja and G. Jacucci, "Neuroadaptive Meditation in the Real World," BCIforReal '17 Proceedings of the 2017 ACM Workshop on An Application-oriented Approach to BCI out of the laboratory, pp. 29-33, Marzo 2017.
(20) A. Al-Cahalabi, M. Turner and S. Delamont, The Brain, NewYork: Oneworld Publications, 2006.
(21) S. J., "Técnicas de toma de datos y análisis de Electroencefalografía.," Universidad de Sevilla, Sevilla, España, 2014.
