Sistema y método para la interpretación de la imaginación motora de los movimientos de ponerse de pie y sentarse basado en interfaz cerebro computadora

Brain-computer interface (BCI) systems based on electroencephalography (EEG) and motor imagination (MI), have shown promising advances for the motor rehabilitation of lower extremities. However, in the state of the art there has been little explored about the MR of the lower limb, especially little...

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Autores:: Triana Guzmán, Nayid

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2022

Institución:: Universidad Antonio Nariño

Repositorio:: Repositorio UAN

Idioma:: spa

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dc.title.es_ES.fl_str_mv	Sistema y método para la interpretación de la imaginación motora de los movimientos de ponerse de pie y sentarse basado en interfaz cerebro computadora
title	Sistema y método para la interpretación de la imaginación motora de los movimientos de ponerse de pie y sentarse basado en interfaz cerebro computadora
spellingShingle	Sistema y método para la interpretación de la imaginación motora de los movimientos de ponerse de pie y sentarse basado en interfaz cerebro computadora interfaz cerebro-computadora (ICC) computadora (ICC), electroencefalografía (EEG), imaginación motora (IM), sentarse-pararse, procesamiento digital de señales, reconocimiento de patrones 600 brain-computer interface (BCI), electroencephalography (EEG motor imagery (MI), sit-stand, digital signal processing, pattern recognition
title_short	Sistema y método para la interpretación de la imaginación motora de los movimientos de ponerse de pie y sentarse basado en interfaz cerebro computadora
title_full	Sistema y método para la interpretación de la imaginación motora de los movimientos de ponerse de pie y sentarse basado en interfaz cerebro computadora
title_fullStr	Sistema y método para la interpretación de la imaginación motora de los movimientos de ponerse de pie y sentarse basado en interfaz cerebro computadora
title_full_unstemmed	Sistema y método para la interpretación de la imaginación motora de los movimientos de ponerse de pie y sentarse basado en interfaz cerebro computadora
title_sort	Sistema y método para la interpretación de la imaginación motora de los movimientos de ponerse de pie y sentarse basado en interfaz cerebro computadora
dc.creator.fl_str_mv	Triana Guzmán, Nayid
dc.contributor.advisor.spa.fl_str_mv	Jutinico Alarcón, Andrés Leonardo Orjuela Cañón, Álvaro David Reyes Guzmán, Edwin Alfredo
dc.contributor.author.spa.fl_str_mv	Triana Guzmán, Nayid
dc.subject.es_ES.fl_str_mv	interfaz cerebro-computadora (ICC) computadora (ICC), electroencefalografía (EEG), imaginación motora (IM), sentarse-pararse, procesamiento digital de señales, reconocimiento de patrones
topic	interfaz cerebro-computadora (ICC) computadora (ICC), electroencefalografía (EEG), imaginación motora (IM), sentarse-pararse, procesamiento digital de señales, reconocimiento de patrones 600 brain-computer interface (BCI), electroencephalography (EEG motor imagery (MI), sit-stand, digital signal processing, pattern recognition
dc.subject.ddc.es_ES.fl_str_mv	600
dc.subject.keyword.es_ES.fl_str_mv	brain-computer interface (BCI), electroencephalography (EEG motor imagery (MI), sit-stand, digital signal processing, pattern recognition
description	Brain-computer interface (BCI) systems based on electroencephalography (EEG) and motor imagination (MI), have shown promising advances for the motor rehabilitation of lower extremities. However, in the state of the art there has been little explored about the MR of the lower limb, especially little is known about MR for standing and sitting. By Therefore, this paper presents an EEG-based ICC system for MI interpretation of these types of movements. The purpose of this system is to restore some mobility to people with disorders severe neuromuscular disorders that cannot exert the force required to move the physical interface (mouse, keyboard, joystick, microphone, or other peripherals) that use standing devices to perform transition from sitting to bipedal position
publishDate	2022
dc.date.issued.spa.fl_str_mv	2022-12-07
dc.date.accessioned.none.fl_str_mv	2023-05-18T20:45:06Z
dc.date.available.none.fl_str_mv	2023-05-18T20:45:06Z
dc.type.spa.fl_str_mv	Tesis y disertaciones (Maestría y/o Doctorado)
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dc.type.local.spa.fl_str_mv	Tesis/Trabajo de grado - Monografía - Doctorado
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dc.identifier.bibliographicCitation.spa.fl_str_mv	Abdulkader, S. N., Atia, A., & Mostafa, M.-S. M. (2015). Brain computer interfacing: Applications and challenges. Egyptian Informatics Journal, 16(2), 213–230. https://doi.org/10.1016/j.eij.2015.06.002 Abiri, R., Borhani, S., Sellers, E. W., Jiang, Y., & Zhao, X. (2019). A comprehensive review of EEG-based brain–computer interface paradigms. Journal of Neural Engineering, 16(1), 1–43. https://doi.org/10.1088/1741-2552/aaf12e Aggarwal, S., & Chugh, N. (2019). Signal processing techniques for motor imagery brain computer interface: A review. Array, 1–2, 1–12. https://doi.org/10.1016/j.array.2019.100003 Aggarwal, S., & Chugh, N. (2022). Review of Machine Learning Techniques for EEG Based Brain Computer Interface. Archives of Computational Methods in Engineering, 29(5), 3001–3020. https://doi.org/10.1007/s11831-021-09684-6 Ahn, M., Lee, M., Choi, J., & Jun, S. (2014). A Review of Brain-Computer Interface Games and an Opinion Survey from Researchers, Developers and Users. Sensors, 14(8), 14601–14633. https://doi.org/10.3390/s140814601 Ajiboye, A. B., Willett, F. R., Young, D. R., Memberg, W. D., Murphy, B. A., Miller, J. P., Walter, B. L., Sweet, J. A., Hoyen, H. A., Keith, M. W., Peckham, P. H., Simeral, J. D., Donoghue, J. P., Hochberg, L. R., & Kirsch, R. F. (2017). Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration. The Lancet, 389(10081), 1821–1830. https://doi.org/10.1016/S0140- 6736(17)30601-3 Al-Fahoum, A. S., & Al-Fraihat, A. A. (2014). Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains. ISRN Neuroscience, 2014, 1–7. https://doi.org/10.1155/2014/730218 Al-Saegh, A., Dawwd, S. A., & Abdul-Jabbar, J. M. (2021). Deep learning for motor imagery EEGbased classification: A review. Biomedical Signal Processing and Control, 63, 1–21. https://doi.org/10.1016/j.bspc.2020.102172 Allison, B. Z., & Neuper, C. (2010). Could Anyone Use a BCI? In D. S. Tan & A. Nijholt (Eds.), Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction (1st ed., pp. 35–54). Springer, London. https://doi.org/10.1007/978-1-84996-272-8_3 Alyasseri, Z. A. A., Khadeer, A. T., Al-Betar, M. A., Abasi, A., Makhadmeh, S., & Ali, N. S. (2019). The Effects of EEG Feature Extraction Using Multi-Wavelet Decomposition for Mental Tasks Classification. Proceedings of the International Conference on Information and Communication Technology - ICICT ’19, 139–146. https://doi.org/10.1145/3321289.3321327
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identifier_str_mv	Abdulkader, S. N., Atia, A., & Mostafa, M.-S. M. (2015). Brain computer interfacing: Applications and challenges. Egyptian Informatics Journal, 16(2), 213–230. https://doi.org/10.1016/j.eij.2015.06.002 Abiri, R., Borhani, S., Sellers, E. W., Jiang, Y., & Zhao, X. (2019). A comprehensive review of EEG-based brain–computer interface paradigms. Journal of Neural Engineering, 16(1), 1–43. https://doi.org/10.1088/1741-2552/aaf12e Aggarwal, S., & Chugh, N. (2019). Signal processing techniques for motor imagery brain computer interface: A review. Array, 1–2, 1–12. https://doi.org/10.1016/j.array.2019.100003 Aggarwal, S., & Chugh, N. (2022). Review of Machine Learning Techniques for EEG Based Brain Computer Interface. Archives of Computational Methods in Engineering, 29(5), 3001–3020. https://doi.org/10.1007/s11831-021-09684-6 Ahn, M., Lee, M., Choi, J., & Jun, S. (2014). A Review of Brain-Computer Interface Games and an Opinion Survey from Researchers, Developers and Users. Sensors, 14(8), 14601–14633. https://doi.org/10.3390/s140814601 Ajiboye, A. B., Willett, F. R., Young, D. R., Memberg, W. D., Murphy, B. A., Miller, J. P., Walter, B. L., Sweet, J. A., Hoyen, H. A., Keith, M. W., Peckham, P. H., Simeral, J. D., Donoghue, J. P., Hochberg, L. R., & Kirsch, R. F. (2017). Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration. The Lancet, 389(10081), 1821–1830. https://doi.org/10.1016/S0140- 6736(17)30601-3 Al-Fahoum, A. S., & Al-Fraihat, A. A. (2014). Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains. ISRN Neuroscience, 2014, 1–7. https://doi.org/10.1155/2014/730218 Al-Saegh, A., Dawwd, S. A., & Abdul-Jabbar, J. M. (2021). Deep learning for motor imagery EEGbased classification: A review. Biomedical Signal Processing and Control, 63, 1–21. https://doi.org/10.1016/j.bspc.2020.102172 Allison, B. Z., & Neuper, C. (2010). Could Anyone Use a BCI? In D. S. Tan & A. Nijholt (Eds.), Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction (1st ed., pp. 35–54). Springer, London. https://doi.org/10.1007/978-1-84996-272-8_3 Alyasseri, Z. A. A., Khadeer, A. T., Al-Betar, M. A., Abasi, A., Makhadmeh, S., & Ali, N. S. (2019). The Effects of EEG Feature Extraction Using Multi-Wavelet Decomposition for Mental Tasks Classification. Proceedings of the International Conference on Information and Communication Technology - ICICT ’19, 139–146. https://doi.org/10.1145/3321289.3321327 instname:Universidad Antonio Nariño reponame:Repositorio Institucional UAN repourl:https://repositorio.uan.edu.co/
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dc.publisher.spa.fl_str_mv	Universidad Antonio Nariño
dc.publisher.program.spa.fl_str_mv	Doctorado en Ciencia Aplicada
dc.publisher.faculty.spa.fl_str_mv	Doctorado en Ciencia Aplicada
dc.publisher.campus.spa.fl_str_mv	Bogotá - Circunvalar
institution	Universidad Antonio Nariño
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spelling	Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)Acceso abiertohttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Jutinico Alarcón, Andrés LeonardoOrjuela Cañón, Álvaro DavidReyes Guzmán, Edwin AlfredoTriana Guzmán, Nayid130018257362023-05-18T20:45:06Z2023-05-18T20:45:06Z2022-12-07http://repositorio.uan.edu.co/handle/123456789/8023Abdulkader, S. N., Atia, A., & Mostafa, M.-S. M. (2015). Brain computer interfacing: Applications and challenges. Egyptian Informatics Journal, 16(2), 213–230. https://doi.org/10.1016/j.eij.2015.06.002Abiri, R., Borhani, S., Sellers, E. W., Jiang, Y., & Zhao, X. (2019). A comprehensive review of EEG-based brain–computer interface paradigms. Journal of Neural Engineering, 16(1), 1–43. https://doi.org/10.1088/1741-2552/aaf12eAggarwal, S., & Chugh, N. (2019). Signal processing techniques for motor imagery brain computer interface: A review. Array, 1–2, 1–12. https://doi.org/10.1016/j.array.2019.100003Aggarwal, S., & Chugh, N. (2022). Review of Machine Learning Techniques for EEG Based Brain Computer Interface. Archives of Computational Methods in Engineering, 29(5), 3001–3020. https://doi.org/10.1007/s11831-021-09684-6Ahn, M., Lee, M., Choi, J., & Jun, S. (2014). A Review of Brain-Computer Interface Games and an Opinion Survey from Researchers, Developers and Users. Sensors, 14(8), 14601–14633. https://doi.org/10.3390/s140814601Ajiboye, A. B., Willett, F. R., Young, D. R., Memberg, W. D., Murphy, B. A., Miller, J. P., Walter, B. L., Sweet, J. A., Hoyen, H. A., Keith, M. W., Peckham, P. H., Simeral, J. D., Donoghue, J. P., Hochberg, L. R., & Kirsch, R. F. (2017). Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration. The Lancet, 389(10081), 1821–1830. https://doi.org/10.1016/S0140- 6736(17)30601-3Al-Fahoum, A. S., & Al-Fraihat, A. A. (2014). Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains. ISRN Neuroscience, 2014, 1–7. https://doi.org/10.1155/2014/730218Al-Saegh, A., Dawwd, S. A., & Abdul-Jabbar, J. M. (2021). Deep learning for motor imagery EEGbased classification: A review. Biomedical Signal Processing and Control, 63, 1–21. https://doi.org/10.1016/j.bspc.2020.102172Allison, B. Z., & Neuper, C. (2010). Could Anyone Use a BCI? In D. S. Tan & A. Nijholt (Eds.), Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction (1st ed., pp. 35–54). Springer, London. https://doi.org/10.1007/978-1-84996-272-8_3Alyasseri, Z. A. A., Khadeer, A. T., Al-Betar, M. A., Abasi, A., Makhadmeh, S., & Ali, N. S. (2019). The Effects of EEG Feature Extraction Using Multi-Wavelet Decomposition for Mental Tasks Classification. Proceedings of the International Conference on Information and Communication Technology - ICICT ’19, 139–146. https://doi.org/10.1145/3321289.3321327instname:Universidad Antonio Nariñoreponame:Repositorio Institucional UANrepourl:https://repositorio.uan.edu.co/Brain-computer interface (BCI) systems based on electroencephalography (EEG) and motor imagination (MI), have shown promising advances for the motor rehabilitation of lower extremities. However, in the state of the art there has been little explored about the MR of the lower limb, especially little is known about MR for standing and sitting. By Therefore, this paper presents an EEG-based ICC system for MI interpretation of these types of movements. The purpose of this system is to restore some mobility to people with disorders severe neuromuscular disorders that cannot exert the force required to move the physical interface (mouse, keyboard, joystick, microphone, or other peripherals) that use standing devices to perform transition from sitting to bipedal positionLos sistemas de interfaz cerebro-computadora (ICC) basados en electroencefalografía (EEG) e imaginación motora (IM), han mostrado avances prometedores para la rehabilitación motriz de las extremidades inferiores. Sin embargo, en el estado del arte ha sido poco explorado sobre la IM del miembro inferior, especialmente se sabe poco acerca de la IM para la bipedestación y la sedestación. Por lo tanto, este trabajo presenta un sistema de ICC basado en EEG para la interpretación de la IM de estos tipos de movimientos. El propósito de este sistema es devolver cierta movilidad a personas con trastornos neuromusculares graves que no pueden imprimir la fuerza que se requiere para mover la interfaz física (ratón, teclado, joystick, micrófono, u otros periféricos) que usan dispositivos bipedestadores para realizar la transición de la posición sedente-bípedaDoctor(a) en Ciencia AplicadaDoctoradoPresencialInvestigaciónspaUniversidad Antonio NariñoDoctorado en Ciencia AplicadaDoctorado en Ciencia AplicadaBogotá - Circunvalarinterfaz cerebro-computadora (ICC)computadora (ICC), electroencefalografía (EEG),imaginación motora (IM), sentarse-pararse, procesamiento digital de señales, reconocimiento de patrones600brain-computer interface (BCI), electroencephalography (EEGmotor imagery (MI), sit-stand, digital signal processing, pattern recognitionSistema y método para la interpretación de la imaginación motora de los movimientos de ponerse de pie y sentarse basado en interfaz cerebro computadoraTesis y disertaciones (Maestría y/o Doctorado)http://purl.org/coar/resource_type/c_db06http://purl.org/coar/version/c_970fb48d4fbd8a85Tesis/Trabajo de grado - Monografía - DoctoradoEspecializadaORIGINAL2022_NayidTrianaGuzman.pdf2022_NayidTrianaGuzman.pdfTesis Doctorado Nayid Trianaapplication/pdf3344652https://repositorio.uan.edu.co/bitstreams/e48456a4-9506-4319-b31c-1effd40d3398/download60e9fe3378e878e56aee08e25fff114dMD512022_NayidTrianaGuzma_Autorizacion.pdf2022_NayidTrianaGuzma_Autorizacion.pdfFormato autorización publicación tesisapplication/pdf538369https://repositorio.uan.edu.co/bitstreams/36538b80-55d2-4f07-a9c5-edf5d5301c02/download37338645371ae16e52ff51160065f54eMD522022_NayidTrianaGuzma_Acta.pdf2022_NayidTrianaGuzma_Acta.pdfActa de sustentación tesisapplication/pdf549127https://repositorio.uan.edu.co/bitstreams/e793c3d8-df0b-46f6-b053-1750286901e5/download9455262b2c54591ae0058870435e7bd0MD53TEXT2022_NayidTrianaGuzman.pdf.txt2022_NayidTrianaGuzman.pdf.txtExtracted texttext/plain101806https://repositorio.uan.edu.co/bitstreams/57b19d8e-2a69-4d76-beac-0e9eb2e49d89/download77af881be083db3205b450db3345cd7cMD542022_NayidTrianaGuzma_Autorizacion.pdf.txt2022_NayidTrianaGuzma_Autorizacion.pdf.txtExtracted texttext/plain6https://repositorio.uan.edu.co/bitstreams/d80f7444-a1a4-4c3b-aa34-1013d28c958f/download6d93d3216dc4a7f5df47d4876fbec4d3MD562022_NayidTrianaGuzma_Acta.pdf.txt2022_NayidTrianaGuzma_Acta.pdf.txtExtracted texttext/plain270https://repositorio.uan.edu.co/bitstreams/40d22dba-e7fc-445e-8153-17c7cf0ffc40/download40733989ee73f97ecd5aaa4fb81db7a8MD58THUMBNAIL2022_NayidTrianaGuzman.pdf.jpg2022_NayidTrianaGuzman.pdf.jpgGenerated Thumbnailimage/jpeg6788https://repositorio.uan.edu.co/bitstreams/0d95403e-1ef8-4965-b81c-893276f6f3ff/download3d787a325add37d07d29dc8baf360190MD552022_NayidTrianaGuzma_Autorizacion.pdf.jpg2022_NayidTrianaGuzma_Autorizacion.pdf.jpgGenerated Thumbnailimage/jpeg16814https://repositorio.uan.edu.co/bitstreams/115e6f67-2b3d-44da-8363-c7ca601bb720/download2dcb2b114e7130b5b47a9e5c16797a03MD572022_NayidTrianaGuzma_Acta.pdf.jpg2022_NayidTrianaGuzma_Acta.pdf.jpgGenerated Thumbnailimage/jpeg18590https://repositorio.uan.edu.co/bitstreams/22949f7d-e41c-4458-9f75-bac14842366f/download6287563a45757225c7d6eefae7575eaaMD59123456789/8023oai:repositorio.uan.edu.co:123456789/80232024-10-09 23:22:01.313https://creativecommons.org/licenses/by-nc-nd/4.0/Acceso abiertoopen.accesshttps://repositorio.uan.edu.coRepositorio Institucional UANalertas.repositorio@uan.edu.co

Sistema y método para la interpretación de la imaginación motora de los movimientos de ponerse de pie y sentarse basado en interfaz cerebro computadora

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