Identification of motor imagery tasks using power-based connectivity descriptors from EEG signals

In recent years, functional connectivity has been studied through electroencephalography signals to analyze the patterns generated by the electrical conductions of the brain. In BCI systems, the paradigm of motor imagery has been used to generate patterns to identify the user’s intention. However, t...

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Autores:: Guerrero Méndez, Cristian David

Tipo de recurso:: Trabajo de grado de pregrado

Fecha de publicación:: 2021

Institución:: Universidad Antonio Nariño

Repositorio:: Repositorio UAN

Idioma:: eng

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dc.title.es_ES.fl_str_mv	Identification of motor imagery tasks using power-based connectivity descriptors from EEG signals
title	Identification of motor imagery tasks using power-based connectivity descriptors from EEG signals
spellingShingle	Identification of motor imagery tasks using power-based connectivity descriptors from EEG signals Electroencefalografía (EEG) Cerebro- Interfaz de computadora Conectividad basada en energía Conectividad funcional 621.7 Electroencephalography (EEG) Brain- Computer Interface (BCI) Power-Based Connectivity Functional Connectivity
title_short	Identification of motor imagery tasks using power-based connectivity descriptors from EEG signals
title_full	Identification of motor imagery tasks using power-based connectivity descriptors from EEG signals
title_fullStr	Identification of motor imagery tasks using power-based connectivity descriptors from EEG signals
title_full_unstemmed	Identification of motor imagery tasks using power-based connectivity descriptors from EEG signals
title_sort	Identification of motor imagery tasks using power-based connectivity descriptors from EEG signals
dc.creator.fl_str_mv	Guerrero Méndez, Cristian David
dc.contributor.advisor.spa.fl_str_mv	Ruiz Olaya, Andrés Felipe
dc.contributor.author.spa.fl_str_mv	Guerrero Méndez, Cristian David
dc.subject.es_ES.fl_str_mv	Electroencefalografía (EEG) Cerebro- Interfaz de computadora Conectividad basada en energía Conectividad funcional
topic	Electroencefalografía (EEG) Cerebro- Interfaz de computadora Conectividad basada en energía Conectividad funcional 621.7 Electroencephalography (EEG) Brain- Computer Interface (BCI) Power-Based Connectivity Functional Connectivity
dc.subject.ddc.es_ES.fl_str_mv	621.7
dc.subject.keyword.es_ES.fl_str_mv	Electroencephalography (EEG) Brain- Computer Interface (BCI) Power-Based Connectivity Functional Connectivity
description	In recent years, functional connectivity has been studied through electroencephalography signals to analyze the patterns generated by the electrical conductions of the brain. In BCI systems, the paradigm of motor imagery has been used to generate patterns to identify the user’s intention. However, the study of techniques that allow the correct identification and classification of such intention is still a challenge due to the low performance of algorithms for rehabilitation engineering applications. This study addresses the problem of binary identification of left and right-hand opening and closing motor imagery tasks. A method called Power-Based Connectivity (PBC) is proposed that correlates two reference channels in the central cortex (C3 and C4) with other channels located in the central area of the brain. The methods were evaluated using an EEG dataset of six subjects with no previous experience in BCI systems built at the Antonio Narino University. The method was compared ˜ with a standard feature extraction method based on Power Spectral Density (PSD). It was used for evaluation accuracy and cohen’s Kappa coefficients metrics. Maximum accuracy and cohen’s Kappa coefficient of 0.7733 and 0.5488, respectively, were obtained using the Linear Discriminant Analysis (LDA) classifier. Finally, the proposed method was superior in performance and presents significant results in the alpha (α) frequency band and the combination of alpha (α) and beta (β). This leads to the conclusion that the proposed method is adequate for user intent identification in a motor imagery-based BCI system of users with no prior experience.
publishDate	2021
dc.date.issued.spa.fl_str_mv	2021-11-12
dc.date.accessioned.none.fl_str_mv	2022-05-19T19:09:26Z
dc.date.available.none.fl_str_mv	2022-05-19T19:09:26Z
dc.type.spa.fl_str_mv	Trabajo de grado (Pregrado y/o Especialización)
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dc.identifier.bibliographicCitation.spa.fl_str_mv	G. Pfurtscheller and C. Neuper, “Motor imagery and direct braincomputer communication,” Proceedings of the IEEE, vol. 89, no. 7, pp. 1123–1134, 2001. J. Becedas, “Brain–machine interfaces: Basis and advances,” IEEE Transactions on Systems, Man and Cybernetics-Part C: Applications and Reviews, vol. 42, no. 6, pp. 825–836, 2012 D. McFarland and J. Wolpaw, “Brain-computer interfaces for communication and control.” Communications of the ACM, vol. 5, pp. 60–66, 2011. U. Chaudary, N. Birbaumer, and A. Ramos, “Brain-computer interfaces for communication and rehabilitation.” Nature Reviews Neurology, vol. 12, pp. 513–525, 2016 A. Rezeika, M. Benda, P. Stawicki, F. Gembler, F. Saboor, and I. Volosyak, “Brain-computerinterface spellers: A review.” Brain Sciences, vol. 8, pp. 1–38, 2018. I. Gaudet, A. Husser, P. Vannasing, and A. Gallagher, “Functional brain ¨ connectivity of language functions in children revealed by eeg and meg: A systematic review,” Frontiers in human neuroscience, vol. 14, p. 62, 2020 V. Sakkalis, “Review of advanced techniques for the estimation of brain connectivity measured with eeg/meg,” Computers in biology and medicine, vol. 41, no. 12, pp. 1110–1117, 2011. D. Krusienski, D. McFarland, and J. Wolpaw, “Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based braincomputer interface,” Brain Research Bulletin, vol. 87, p. 130–134, 2012 C. Kim, J. Sun, D. Liu, and Q. Wang, “An effective feature extraction method by power spectral density of eeg signal for 2-class motor imagery-based bci,” Medical Biological Engineering Computing, vol. 56, pp. 1–14, 2018. X. Qiao, Y. Wang, D. Li, and L. Tian, “Feature extraction and classifier evaluation of eeg for imaginary hand movements,” in 2010 Sixth International Conference on Natural Computation, vol. 4. IEEE, 2010, pp. 2112–2116.
dc.identifier.instname.spa.fl_str_mv	instname:Universidad Antonio Nariño
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identifier_str_mv	G. Pfurtscheller and C. Neuper, “Motor imagery and direct braincomputer communication,” Proceedings of the IEEE, vol. 89, no. 7, pp. 1123–1134, 2001. J. Becedas, “Brain–machine interfaces: Basis and advances,” IEEE Transactions on Systems, Man and Cybernetics-Part C: Applications and Reviews, vol. 42, no. 6, pp. 825–836, 2012 D. McFarland and J. Wolpaw, “Brain-computer interfaces for communication and control.” Communications of the ACM, vol. 5, pp. 60–66, 2011. U. Chaudary, N. Birbaumer, and A. Ramos, “Brain-computer interfaces for communication and rehabilitation.” Nature Reviews Neurology, vol. 12, pp. 513–525, 2016 A. Rezeika, M. Benda, P. Stawicki, F. Gembler, F. Saboor, and I. Volosyak, “Brain-computerinterface spellers: A review.” Brain Sciences, vol. 8, pp. 1–38, 2018. I. Gaudet, A. Husser, P. Vannasing, and A. Gallagher, “Functional brain ¨ connectivity of language functions in children revealed by eeg and meg: A systematic review,” Frontiers in human neuroscience, vol. 14, p. 62, 2020 V. Sakkalis, “Review of advanced techniques for the estimation of brain connectivity measured with eeg/meg,” Computers in biology and medicine, vol. 41, no. 12, pp. 1110–1117, 2011. D. Krusienski, D. McFarland, and J. Wolpaw, “Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based braincomputer interface,” Brain Research Bulletin, vol. 87, p. 130–134, 2012 C. Kim, J. Sun, D. Liu, and Q. Wang, “An effective feature extraction method by power spectral density of eeg signal for 2-class motor imagery-based bci,” Medical Biological Engineering Computing, vol. 56, pp. 1–14, 2018. X. Qiao, Y. Wang, D. Li, and L. Tian, “Feature extraction and classifier evaluation of eeg for imaginary hand movements,” in 2010 Sixth International Conference on Natural Computation, vol. 4. IEEE, 2010, pp. 2112–2116. instname:Universidad Antonio Nariño reponame:Repositorio Institucional UAN repourl:https://repositorio.uan.edu.co/
dc.language.iso.spa.fl_str_mv	eng
language	eng
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dc.publisher.spa.fl_str_mv	Universidad Antonio Nariño
dc.publisher.program.spa.fl_str_mv	Ingeniería Biomédica
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería Mecánica, Electrónica y Biomédica
dc.publisher.campus.spa.fl_str_mv	Bogotá - Sur
institution	Universidad Antonio Nariño
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spelling	Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)Acceso a solo metadatoshttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbRuiz Olaya, Andrés FelipeGuerrero Méndez, Cristian David105618235532022-05-19T19:09:26Z2022-05-19T19:09:26Z2021-11-12http://repositorio.uan.edu.co/handle/123456789/6578G. Pfurtscheller and C. Neuper, “Motor imagery and direct braincomputer communication,” Proceedings of the IEEE, vol. 89, no. 7, pp. 1123–1134, 2001.J. Becedas, “Brain–machine interfaces: Basis and advances,” IEEE Transactions on Systems, Man and Cybernetics-Part C: Applications and Reviews, vol. 42, no. 6, pp. 825–836, 2012D. McFarland and J. Wolpaw, “Brain-computer interfaces for communication and control.” Communications of the ACM, vol. 5, pp. 60–66, 2011.U. Chaudary, N. Birbaumer, and A. Ramos, “Brain-computer interfaces for communication and rehabilitation.” Nature Reviews Neurology, vol. 12, pp. 513–525, 2016A. Rezeika, M. Benda, P. Stawicki, F. Gembler, F. Saboor, and I. Volosyak, “Brain-computerinterface spellers: A review.” Brain Sciences, vol. 8, pp. 1–38, 2018.I. Gaudet, A. Husser, P. Vannasing, and A. Gallagher, “Functional brain ¨ connectivity of language functions in children revealed by eeg and meg: A systematic review,” Frontiers in human neuroscience, vol. 14, p. 62, 2020V. Sakkalis, “Review of advanced techniques for the estimation of brain connectivity measured with eeg/meg,” Computers in biology and medicine, vol. 41, no. 12, pp. 1110–1117, 2011.D. Krusienski, D. McFarland, and J. Wolpaw, “Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based braincomputer interface,” Brain Research Bulletin, vol. 87, p. 130–134, 2012C. Kim, J. Sun, D. Liu, and Q. Wang, “An effective feature extraction method by power spectral density of eeg signal for 2-class motor imagery-based bci,” Medical Biological Engineering Computing, vol. 56, pp. 1–14, 2018.X. Qiao, Y. Wang, D. Li, and L. Tian, “Feature extraction and classifier evaluation of eeg for imaginary hand movements,” in 2010 Sixth International Conference on Natural Computation, vol. 4. IEEE, 2010, pp. 2112–2116.instname:Universidad Antonio Nariñoreponame:Repositorio Institucional UANrepourl:https://repositorio.uan.edu.co/In recent years, functional connectivity has been studied through electroencephalography signals to analyze the patterns generated by the electrical conductions of the brain. In BCI systems, the paradigm of motor imagery has been used to generate patterns to identify the user’s intention. However, the study of techniques that allow the correct identification and classification of such intention is still a challenge due to the low performance of algorithms for rehabilitation engineering applications. This study addresses the problem of binary identification of left and right-hand opening and closing motor imagery tasks. A method called Power-Based Connectivity (PBC) is proposed that correlates two reference channels in the central cortex (C3 and C4) with other channels located in the central area of the brain. The methods were evaluated using an EEG dataset of six subjects with no previous experience in BCI systems built at the Antonio Narino University. The method was compared ˜ with a standard feature extraction method based on Power Spectral Density (PSD). It was used for evaluation accuracy and cohen’s Kappa coefficients metrics. Maximum accuracy and cohen’s Kappa coefficient of 0.7733 and 0.5488, respectively, were obtained using the Linear Discriminant Analysis (LDA) classifier. Finally, the proposed method was superior in performance and presents significant results in the alpha (α) frequency band and the combination of alpha (α) and beta (β). This leads to the conclusion that the proposed method is adequate for user intent identification in a motor imagery-based BCI system of users with no prior experience.En los últimos años, la conectividad funcional ha sido estudiado a través de señales de electroencefalografía para analizar la patrones generados por las conducciones eléctricas del cerebro. En los sistemas BCI, se ha utilizado el paradigma de la imaginería motora generar patrones para identificar la intención del usuario. Sin embargo, el estudio de técnicas que permitan la correcta identificación y clasificación de tal intención sigue siendo un desafío debido a la baja rendimiento de algoritmos para aplicaciones de ingeniería de rehabilitación. Este estudio aborda el problema de la identificación binaria. de tareas de imágenes motoras de apertura y cierre de mano izquierda y derecha. Se propone un método llamado Conectividad basada en energía (PBC) que correlaciona dos canales de referencia en la corteza central (C3 y C4) con otros canales ubicados en la zona central del cerebro. Los métodos se evaluaron utilizando un conjunto de datos de EEG de seis sujetos sin experiencia previa en sistemas BCI construidos en la Universidad Antonio Nariño. Se comparó el método ˜ con un método de extracción de características estándar basado en Power Densidad espectral (PSD). Se utilizó para evaluar la precisión. y las métricas de los coeficientes Kappa de Cohen. Máxima precisión y coeficiente Kappa de Cohen de 0,7733 y 0,5488, respectivamente, fueron obtenidos mediante el clasificador Análisis Discriminante Lineal (LDA). Finalmente, el método propuesto fue superior en rendimiento y presenta resultados significativos en la banda de frecuencia alfa (α) y la combinación de alfa (α) y beta (β). Esto lleva a la conclusión de que el método propuesto es adecuado para la intención del usuario identificación en un sistema BCI basado en imágenes motoras de usuarios sin experiencia previa.Ingeniero(a) Biomédico(a)PregradoPresencialInvestigaciónengUniversidad Antonio NariñoIngeniería BiomédicaFacultad de Ingeniería Mecánica, Electrónica y BiomédicaBogotá - SurElectroencefalografía (EEG)Cerebro- Interfaz de computadoraConectividad basada en energíaConectividad funcional621.7Electroencephalography (EEG)Brain- Computer Interface (BCI)Power-Based ConnectivityFunctional ConnectivityIdentification of motor imagery tasks using power-based connectivity descriptors from EEG signalsTrabajo de grado (Pregrado y/o Especialización)http://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/version/c_970fb48d4fbd8a85EspecializadaORIGINAL2021_CristianDavidGuerreroMéndez_Acta.pdf2021_CristianDavidGuerreroMéndez_Acta.pdfActa Guerreroapplication/pdf291456https://repositorio.uan.edu.co/bitstreams/ba8a67ee-8268-4331-b0dc-7d0f1f634358/download435943e748375a271e5d04788d73fa25MD512021_CristianDavidGuerreroMéndez.pdf2021_CristianDavidGuerreroMéndez.pdfTrabajo de gradoapplication/pdf1184052https://repositorio.uan.edu.co/bitstreams/fc5f8288-f925-4597-bfdc-e98d25317a99/download017861a2b259795ee0dced9d1067acccMD522021_CristianDavidGuerreroMéndez_Autorización.pdf2021_CristianDavidGuerreroMéndez_Autorización.pdfAutorización de autoresapplication/pdf1365762https://repositorio.uan.edu.co/bitstreams/1e3c2de0-f6df-4acc-a097-fd98c30788fb/download352c288267e29fe1bff5e9da8a72dcf7MD54CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8811https://repositorio.uan.edu.co/bitstreams/e3fc2ce9-0285-427c-b32d-53777e556db2/download9868ccc48a14c8d591352b6eaf7f6239MD55123456789/6578oai:repositorio.uan.edu.co:123456789/65782024-10-09 23:37:07.299https://creativecommons.org/licenses/by-nc-nd/4.0/Acceso a solo metadatosrestrictedhttps://repositorio.uan.edu.coRepositorio Institucional UANalertas.repositorio@uan.edu.co

Identification of motor imagery tasks using power-based connectivity descriptors from EEG signals

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