Algoritmo para la clasificación en línea de fonemas de habla silenciosa utilizando un sistema embebido
ilustraciones, fotografías, graficas
- Autores:
-
Caballero López, Julian David
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/82171
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
Procesamiento Automatizado de Datos
Electronic Data Processing
EMD
MEMD
SVD
PLV
LDA
MULTICLASS CLASSIFIERS
CLASIFICADORES MULTICLASE
Programación informática
Computer programming
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
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Algoritmo para la clasificación en línea de fonemas de habla silenciosa utilizando un sistema embebido |
| dc.title.translated.eng.fl_str_mv |
Algorithm for online classification of silent speech phonemes using an embedded system |
| title |
Algoritmo para la clasificación en línea de fonemas de habla silenciosa utilizando un sistema embebido |
| spellingShingle |
Algoritmo para la clasificación en línea de fonemas de habla silenciosa utilizando un sistema embebido 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Procesamiento Automatizado de Datos Electronic Data Processing EMD MEMD SVD PLV LDA MULTICLASS CLASSIFIERS CLASIFICADORES MULTICLASE Programación informática Computer programming |
| title_short |
Algoritmo para la clasificación en línea de fonemas de habla silenciosa utilizando un sistema embebido |
| title_full |
Algoritmo para la clasificación en línea de fonemas de habla silenciosa utilizando un sistema embebido |
| title_fullStr |
Algoritmo para la clasificación en línea de fonemas de habla silenciosa utilizando un sistema embebido |
| title_full_unstemmed |
Algoritmo para la clasificación en línea de fonemas de habla silenciosa utilizando un sistema embebido |
| title_sort |
Algoritmo para la clasificación en línea de fonemas de habla silenciosa utilizando un sistema embebido |
| dc.creator.fl_str_mv |
Caballero López, Julian David |
| dc.contributor.advisor.none.fl_str_mv |
Bacca Rodríguez, Jan Villamizar Delgado, Sergio Iván |
| dc.contributor.author.none.fl_str_mv |
Caballero López, Julian David |
| dc.contributor.researchgroup.spa.fl_str_mv |
Grupo de Investigación en Electrónica de Alta Frecuencia y Telecomunicaciones (Cmun) |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores |
| topic |
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Procesamiento Automatizado de Datos Electronic Data Processing EMD MEMD SVD PLV LDA MULTICLASS CLASSIFIERS CLASIFICADORES MULTICLASE Programación informática Computer programming |
| dc.subject.other.spa.fl_str_mv |
Procesamiento Automatizado de Datos |
| dc.subject.other.eng.fl_str_mv |
Electronic Data Processing |
| dc.subject.proposal.eng.fl_str_mv |
EMD MEMD SVD PLV LDA MULTICLASS CLASSIFIERS |
| dc.subject.proposal.spa.fl_str_mv |
CLASIFICADORES MULTICLASE |
| dc.subject.unesco.spa.fl_str_mv |
Programación informática |
| dc.subject.unesco.eng.fl_str_mv |
Computer programming |
| description |
ilustraciones, fotografías, graficas |
| publishDate |
2022 |
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2022-08-29T17:09:02Z |
| dc.date.available.none.fl_str_mv |
2022-08-29T17:09:02Z |
| dc.date.issued.none.fl_str_mv |
2022 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/acceptedVersion |
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Text |
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http://purl.org/redcol/resource_type/TM |
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acceptedVersion |
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https://repositorio.unal.edu.co/handle/unal/82171 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/82171 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
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RedCol LaReferencia |
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S. U. Arias, “Principales retos del Derecho Internacional Humanitario (DIH) en el contexto de postconflicto en Colombia,” 2019. “Víctimas de Minas Antipersonal y Municiones sin Explosionar.” http://www.accioncontraminas.gov.co/estadisticas/Paginas/victimas-minasantipersonal.aspx (accessed Feb. 10, 2020). Kim. Norton, “A brief history of prosthetics,” InMotion, vol. 17, no. 7, pp. 11-3- undefined, 2007. S. Daniela García and V. María José Espinoza, “Avances en prótesis: una mirada al presente y al futuro,” Revista Médica Clínica Las Condes, vol. 25, no. 2, pp. 281– 285, Mar. 2014, doi: 10.1016/S0716-8640(14)70039-2. “History | AOPA – AMERICAN ORTHOTIC & PROSTHETIC ASSOCIATION.” https://www.aopanet.org/about-aopa/history/ (accessed Jul. 11, 2021). J. L. Brito, M. X. Quinde, D. Cuzco, and J. I. Calle, “Estudio del estado del arte de las prótesis de mano,” 2013, Accessed: Jul. 11, 2021. [Online]. Available: http://dspace.ups.edu.ec/handle/123456789/8447 H. A. ROMO., J. C. REALPE, and P. E. JOJOA, “ANÁLISIS DE SEÑALES EMG SUPERFICIALES Y SU APLICACIÓN EN CONTROL DE PRÓTESIS DE MANO,” Avances en Sistemas e Informática, vol. 4, no. 1, Jan. 2007, Accessed: Jul. 11, 2021. [Online]. Available: https://revistas.unal.edu.co/index.php/avances/article/view/9725 A. A. Adewuyi, L. J. Hargrove, and T. A. Kuiken, “Evaluating EMG Feature and Classifier Selection for Application to Partial-Hand Prosthesis Control,” Frontiers in Neurorobotics, vol. 10, p. 15, Oct. 2016, doi: 10.3389/FNBOT.2016.00015. E. Park and S. G. Meek, “Fatigue compensation of the electromyographic signal for prosthetic control and force estimation,” IEEE Transactions on Biomedical Engineering, vol. 40, no. 10, 1993, doi: 10.1109/10.247800. A. Kübler, V. K. Mushahwar, L. R. Hochberg, and J. P. Donoghue, “BCI Meeting 2005 - Workshop on clinical issues and applications,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, pp. 131–134, Jun. 2006, doi: 10.1109/TNSRE.2006.875585. A.-B. Suleiman, T. A. Fathi, A.-B. R. Suleiman, and A.-H. Fatehi, “FEATURES EXTRACTION TECHNIQES OF EEG SIGNAL FOR BCI APPLICATIONS FEATURES EXTRACTION TECHNIQES OF EEG SIGNAL FOR BCI APPLICATIONS”, Accessed: Jul. 11, 2021. [Online]. Available: https://www.researchgate.net/publication/228450475 C. Park, D. Looney, N. ur Rehman, A. Ahrabian, and D. P. Mandic, “Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no. 1, Jan. 2013, doi: 10.1109/TNSRE.2012.2229296. M. Rohm et al., “Hybrid brain–computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury,” Artificial Intelligence in Medicine, vol. 59, no. 2, Oct. 2013, doi: 10.1016/j.artmed.2013.07.004. F. Cincotti et al., “Non-invasive brain–computer interface system: Towards its application as assistive technology,” Brain Research Bulletin, vol. 75, no. 6, Apr. 2008, doi: 10.1016/j.brainresbull.2008.01.007. J. H. Friedman, “Multivariate Adaptive Regression Splines,” https://doi.org/10.1214/aos/1176347963, vol. 19, no. 1, pp. 1–67, Mar. 1991, doi: 10.1214/AOS/1176347963. N. Rehman and D. P. Mandic, “Multivariate empirical mode decomposition,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 466, no. 2117, May 2010, doi: 10.1098/rspa.2009.0502. A. Hemakom, V. Goverdovsky, D. Looney, and D. P. Mandic, “Adaptive-projection intrinsically transformed multivariate empirical mode decomposition in cooperative brain–computer interface applications,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 374, no. 2065, Apr. 2016, doi: 10.1098/rsta.2015.0199. S. Zhao and F. Rudzicz, “Classifying phonological categories in imagined and articulated speech,” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2015-August, pp. 992–996, Aug. 2015, doi: 10.1109/ICASSP.2015.7178118. S. I. Villamizar Delgado, “Development of algorithms to improve the technical efficiency of capturing, processing, and identification of EEG signals in the word imagery task,” Feb. 2020, Accessed: Jul. 11, 2021. [Online]. Available: https://repositorio.unal.edu.co/handle/unal/77829 N. E. Huang et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, 1998, doi: 10.1098/RSPA.1998.0193. Z. Wu and N. E. Huang, “A study of the characteristics of white noise using the empirical mode decomposition method,” Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 460, no. 2046, pp. 1597–1611, Jun. 2004, doi: 10.1098/RSPA.2003.1221. G. Rilling, P. Flandrin, and P. Gonçalvès, “ON EMPIRICAL MODE DECOMPOSITION AND ITS ALGORITHMS”. G. Rilling, P. Flandrin, P. Goncalves, and J. M. Lilly, “Bivariate empirical mode decomposition,” IEEE Signal Processing Letters, vol. 14, no. 12, pp. 936–939, Dec. 2007, doi: 10.1109/LSP.2007.904710. N. Ur Rehman and D. P. Mandic, “Empirical mode decomposition for trivariate signals,” IEEE Transactions on Signal Processing, vol. 58, no. 3 PART 1, pp. 1059– 1068, Mar. 2010, doi: 10.1109/TSP.2009.2033730. J. Fleureau, A. Kachenoura, L. Albera, J. C. Nunes, and L. Senhadji, “Multivariate empirical mode decomposition and application to multichannel filtering,” Signal Processing, vol. 91, no. 12, pp. 2783–2792, Dec. 2011, doi: 10.1016/J.SIGPRO.2011.01.018. X. Lang et al., “Fast Multivariate Empirical Mode Decomposition,” IEEE Access, vol. 6, pp. 65521–65538, 2018, doi: 10.1109/ACCESS.2018.2877150. H. C. Andrews and C. L. Patterson, “Singular Value Decomposition (SVD) Image Coding,” IEEE Transactions on Communications, vol. 24, no. 4, pp. 425–432, 1976, doi: 10.1109/TCOM.1976.1093309. C. Moore, “APPLICATION OF SINGULAR VALUE DECOMPOSITION TO THE DESIGN, ANALYSIS, AND CONTROL OF INDUSTRIAL PROCESSES.,” Proceedings of the American Control Conference, pp. 643–650, 1986, doi: 10.23919/ACC.1986.4789019. R. Izmailov, D. Bassu, A. McIntosh, L. Ness, and D. Shallcross, “Application of multiscale singular vector decomposition to vessel classification in overhead satellite imagery,” Seventh International Conference on Digital Image Processing (ICDIP 2015), vol. 9631, p. 963108, Jul. 2015, doi: 10.1117/12.2196925. “Understanding Singular Value Decomposition and its Application in Data Science | by Reza Bagheri | Towards Data Science.” https://towardsdatascience.com/understanding-singular-value-decomposition-andits-application-in-data-science-388a54be95d (accessed Jul. 11, 2021). “Una introducción a los Árboles de Decisión | DABIA.” https://www.grupodabia.com/post/2020-05-19-arbol-de-decision/ (accessed Jul. 11, 2021). “6 AdaBoost | Modelos Predictivos.” https://fhernanb.github.io/libro_mod_pred/adaboost.html (accessed Jul. 11, 2021). M. Mandal and A. Mukhopadhyay, “An Improved Minimum Redundancy Maximum Relevance Approach for Feature Selection in Gene Expression Data,” Procedia Technology, vol. 10, pp. 20–27, Jan. 2013, doi: 10.1016/J.PROTCY.2013.12.332. S. Ramírez-Gallego et al., “Fast-mRMR: Fast Minimum Redundancy Maximum Relevance Algorithm for High-Dimensional Big Data,” International Journal of Intelligent Systems, vol. 32, no. 2, pp. 134–152, Feb. 2017, doi: 10.1002/INT.21833. P. Bugata and P. Drotar, “On some aspects of minimum redundancy maximum relevance feature selection,” Science China Information Sciences 2019 63:1, vol. 63, no. 1, pp. 1–15, Dec. 2019, doi: 10.1007/S11432-019-2633-Y. A. Atyabi, S. Fitzgibbon, and D. Powers, “The impact of Biasing on Overlapping windows : An EEG study,” Jul. 2012. A. Atyabi, S. P. Fitzgibbon, and D. M. W. Powers, “Multiplication of EEG Samples through Replicating, Biasing, and Overlapping,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7670 LNAI, pp. 209–219, 2012, doi: 10.1007/978-3-642-35139- 6_20. A. Atyabi, S. Fitzgibbon, and D. M. W. Powers, “Biasing the overlapping and nonoverlapping sub-windows of EEG recording,” Jun. 2012. doi: 10.1109/IJCNN.2012.6252465. |
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Universidad Nacional de Colombia |
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Bogotá - Ingeniería - Maestría en Ingeniería - Automatización Industrial |
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Departamento de Ingeniería Eléctrica y Electrónica |
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Facultad de Ingeniería |
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Universidad Nacional de Colombia - Sede Bogotá |
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Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Bacca Rodríguez, Jan6338cbc186b32e40664d0ba50addfae8Villamizar Delgado, Sergio Iván52bdc1aec5b27e412ccd56857b7a7c42Caballero López, Julian David2f3bb34eb85d5fdb7c4a7f95c7f3d7bcGrupo de Investigación en Electrónica de Alta Frecuencia y Telecomunicaciones (Cmun)2022-08-29T17:09:02Z2022-08-29T17:09:02Z2022https://repositorio.unal.edu.co/handle/unal/82171Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, fotografías, graficasDurante el desarrollo de este proyecto se analizaron metodologías para la clasificación de fonemas del habla silenciosa basadas en EMD (Empirical mode decomposition). Este análisis tuvo lugar en la Universidad Nacional de Colombia (UNAL) sede Bogotá, con los datos de la base de datos Emotive-DB tomada con el equipo EMOTIVE EPOC +14, la cual contiene la información de 16 sujetos, mientras pensaban en los fonemas /a/, /e/, /i/, /o/, /u/, y las sílabas /fa/, /pe/, /mi/, /lo/, /ru/. En el proceso, se analizó la afectación en los resultados y el tiempo de procesamiento, en relación con las variables superposición, frecuencia de muestreo, cantidad de canales, entre otras; tras dicho análisis, se seleccionó y trató el número de canales, la distribución de electrodos y los vectores de proyección en la descomposición EMD; con lo cual se logró disminuir el tiempo de procesamiento promedio por trial de 8.73 segundos hasta 0.06 segundos, permitiendo así la posibilidad de implementarse en un sistema en línea. (Texto tomado de la fuente)During the development of this project, methodologies for the classification of phonemes of silent speech based on EMD (Empirical mode decomposition) were analyzed. This analysis took place at the National University of Colombia (UNAL) in Bogotá, with data from the Emotive-DB database taken with the EMOTIVE EPOC + 14 equipment, which contains the information of 16 subjects, while they thought about the phonemes / a /, / e /, / i /, / o /, / u /, and the syllables / fa /, / pe /, / mi /, / lo /, / ru /. In the process, the effect on the results and the processing time were analyzed, in relation to the variables superposition, sampling frequency, number of channels, among others. After said analysis, the number of channels, the electrode distribution and the projection vectors in the EMD decomposition were selected accordingly. As a result, it was possible to reduce the average processing time per-trial from 8.73 seconds to 0.06 seconds, thus allowing the possibility of being implemented in an online system.convocatoria 777 de 2017 de MinCienciasMaestríaMagíster en Ingeniería - Automatización IndustrialClasificación de fonemas de habla silenciosa66 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Automatización IndustrialDepartamento de Ingeniería Eléctrica y ElectrónicaFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresProcesamiento Automatizado de DatosElectronic Data ProcessingEMDMEMDSVDPLVLDAMULTICLASS CLASSIFIERSCLASIFICADORES MULTICLASEProgramación informáticaComputer programmingAlgoritmo para la clasificación en línea de fonemas de habla silenciosa utilizando un sistema embebidoAlgorithm for online classification of silent speech phonemes using an embedded systemTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMRedColLaReferenciaS. U. Arias, “Principales retos del Derecho Internacional Humanitario (DIH) en el contexto de postconflicto en Colombia,” 2019.“Víctimas de Minas Antipersonal y Municiones sin Explosionar.” http://www.accioncontraminas.gov.co/estadisticas/Paginas/victimas-minasantipersonal.aspx (accessed Feb. 10, 2020).Kim. Norton, “A brief history of prosthetics,” InMotion, vol. 17, no. 7, pp. 11-3- undefined, 2007.S. Daniela García and V. María José Espinoza, “Avances en prótesis: una mirada al presente y al futuro,” Revista Médica Clínica Las Condes, vol. 25, no. 2, pp. 281– 285, Mar. 2014, doi: 10.1016/S0716-8640(14)70039-2.“History | AOPA – AMERICAN ORTHOTIC & PROSTHETIC ASSOCIATION.” https://www.aopanet.org/about-aopa/history/ (accessed Jul. 11, 2021).J. L. Brito, M. X. Quinde, D. Cuzco, and J. I. Calle, “Estudio del estado del arte de las prótesis de mano,” 2013, Accessed: Jul. 11, 2021. [Online]. Available: http://dspace.ups.edu.ec/handle/123456789/8447H. A. ROMO., J. C. REALPE, and P. E. JOJOA, “ANÁLISIS DE SEÑALES EMG SUPERFICIALES Y SU APLICACIÓN EN CONTROL DE PRÓTESIS DE MANO,” Avances en Sistemas e Informática, vol. 4, no. 1, Jan. 2007, Accessed: Jul. 11, 2021. [Online]. Available: https://revistas.unal.edu.co/index.php/avances/article/view/9725A. A. Adewuyi, L. J. Hargrove, and T. A. Kuiken, “Evaluating EMG Feature and Classifier Selection for Application to Partial-Hand Prosthesis Control,” Frontiers in Neurorobotics, vol. 10, p. 15, Oct. 2016, doi: 10.3389/FNBOT.2016.00015.E. Park and S. G. Meek, “Fatigue compensation of the electromyographic signal for prosthetic control and force estimation,” IEEE Transactions on Biomedical Engineering, vol. 40, no. 10, 1993, doi: 10.1109/10.247800.A. Kübler, V. K. Mushahwar, L. R. Hochberg, and J. P. Donoghue, “BCI Meeting 2005 - Workshop on clinical issues and applications,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, pp. 131–134, Jun. 2006, doi: 10.1109/TNSRE.2006.875585.A.-B. Suleiman, T. A. Fathi, A.-B. R. Suleiman, and A.-H. Fatehi, “FEATURES EXTRACTION TECHNIQES OF EEG SIGNAL FOR BCI APPLICATIONS FEATURES EXTRACTION TECHNIQES OF EEG SIGNAL FOR BCI APPLICATIONS”, Accessed: Jul. 11, 2021. [Online]. Available: https://www.researchgate.net/publication/228450475C. Park, D. Looney, N. ur Rehman, A. Ahrabian, and D. P. Mandic, “Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no. 1, Jan. 2013, doi: 10.1109/TNSRE.2012.2229296.M. Rohm et al., “Hybrid brain–computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury,” Artificial Intelligence in Medicine, vol. 59, no. 2, Oct. 2013, doi: 10.1016/j.artmed.2013.07.004.F. Cincotti et al., “Non-invasive brain–computer interface system: Towards its application as assistive technology,” Brain Research Bulletin, vol. 75, no. 6, Apr. 2008, doi: 10.1016/j.brainresbull.2008.01.007.J. H. Friedman, “Multivariate Adaptive Regression Splines,” https://doi.org/10.1214/aos/1176347963, vol. 19, no. 1, pp. 1–67, Mar. 1991, doi: 10.1214/AOS/1176347963.N. Rehman and D. P. Mandic, “Multivariate empirical mode decomposition,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 466, no. 2117, May 2010, doi: 10.1098/rspa.2009.0502.A. Hemakom, V. Goverdovsky, D. Looney, and D. P. Mandic, “Adaptive-projection intrinsically transformed multivariate empirical mode decomposition in cooperative brain–computer interface applications,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 374, no. 2065, Apr. 2016, doi: 10.1098/rsta.2015.0199.S. Zhao and F. Rudzicz, “Classifying phonological categories in imagined and articulated speech,” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2015-August, pp. 992–996, Aug. 2015, doi: 10.1109/ICASSP.2015.7178118.S. I. Villamizar Delgado, “Development of algorithms to improve the technical efficiency of capturing, processing, and identification of EEG signals in the word imagery task,” Feb. 2020, Accessed: Jul. 11, 2021. [Online]. Available: https://repositorio.unal.edu.co/handle/unal/77829N. E. Huang et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, 1998, doi: 10.1098/RSPA.1998.0193.Z. Wu and N. E. Huang, “A study of the characteristics of white noise using the empirical mode decomposition method,” Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 460, no. 2046, pp. 1597–1611, Jun. 2004, doi: 10.1098/RSPA.2003.1221.G. Rilling, P. Flandrin, and P. Gonçalvès, “ON EMPIRICAL MODE DECOMPOSITION AND ITS ALGORITHMS”.G. Rilling, P. Flandrin, P. Goncalves, and J. M. Lilly, “Bivariate empirical mode decomposition,” IEEE Signal Processing Letters, vol. 14, no. 12, pp. 936–939, Dec. 2007, doi: 10.1109/LSP.2007.904710.N. Ur Rehman and D. P. Mandic, “Empirical mode decomposition for trivariate signals,” IEEE Transactions on Signal Processing, vol. 58, no. 3 PART 1, pp. 1059– 1068, Mar. 2010, doi: 10.1109/TSP.2009.2033730.J. Fleureau, A. Kachenoura, L. Albera, J. C. Nunes, and L. Senhadji, “Multivariate empirical mode decomposition and application to multichannel filtering,” Signal Processing, vol. 91, no. 12, pp. 2783–2792, Dec. 2011, doi: 10.1016/J.SIGPRO.2011.01.018.X. Lang et al., “Fast Multivariate Empirical Mode Decomposition,” IEEE Access, vol. 6, pp. 65521–65538, 2018, doi: 10.1109/ACCESS.2018.2877150.H. C. Andrews and C. L. Patterson, “Singular Value Decomposition (SVD) Image Coding,” IEEE Transactions on Communications, vol. 24, no. 4, pp. 425–432, 1976, doi: 10.1109/TCOM.1976.1093309.C. Moore, “APPLICATION OF SINGULAR VALUE DECOMPOSITION TO THE DESIGN, ANALYSIS, AND CONTROL OF INDUSTRIAL PROCESSES.,” Proceedings of the American Control Conference, pp. 643–650, 1986, doi: 10.23919/ACC.1986.4789019.R. Izmailov, D. Bassu, A. McIntosh, L. Ness, and D. Shallcross, “Application of multiscale singular vector decomposition to vessel classification in overhead satellite imagery,” Seventh International Conference on Digital Image Processing (ICDIP 2015), vol. 9631, p. 963108, Jul. 2015, doi: 10.1117/12.2196925.“Understanding Singular Value Decomposition and its Application in Data Science | by Reza Bagheri | Towards Data Science.” https://towardsdatascience.com/understanding-singular-value-decomposition-andits-application-in-data-science-388a54be95d (accessed Jul. 11, 2021).“Una introducción a los Árboles de Decisión | DABIA.” https://www.grupodabia.com/post/2020-05-19-arbol-de-decision/ (accessed Jul. 11, 2021).“6 AdaBoost | Modelos Predictivos.” https://fhernanb.github.io/libro_mod_pred/adaboost.html (accessed Jul. 11, 2021).M. Mandal and A. Mukhopadhyay, “An Improved Minimum Redundancy Maximum Relevance Approach for Feature Selection in Gene Expression Data,” Procedia Technology, vol. 10, pp. 20–27, Jan. 2013, doi: 10.1016/J.PROTCY.2013.12.332.S. Ramírez-Gallego et al., “Fast-mRMR: Fast Minimum Redundancy Maximum Relevance Algorithm for High-Dimensional Big Data,” International Journal of Intelligent Systems, vol. 32, no. 2, pp. 134–152, Feb. 2017, doi: 10.1002/INT.21833.P. Bugata and P. Drotar, “On some aspects of minimum redundancy maximum relevance feature selection,” Science China Information Sciences 2019 63:1, vol. 63, no. 1, pp. 1–15, Dec. 2019, doi: 10.1007/S11432-019-2633-Y.A. Atyabi, S. Fitzgibbon, and D. Powers, “The impact of Biasing on Overlapping windows : An EEG study,” Jul. 2012.A. Atyabi, S. P. Fitzgibbon, and D. M. W. Powers, “Multiplication of EEG Samples through Replicating, Biasing, and Overlapping,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7670 LNAI, pp. 209–219, 2012, doi: 10.1007/978-3-642-35139- 6_20.A. Atyabi, S. Fitzgibbon, and D. M. W. Powers, “Biasing the overlapping and nonoverlapping sub-windows of EEG recording,” Jun. 2012. doi: 10.1109/IJCNN.2012.6252465.Desarrollo de una Interfaz Cerebro Computador con señales electroencefalográficas (EEG) que utilice el pensamiento del lenguaje para el control de una prótesis de miembro superior con aplicación a personas discapacitadas con amputaciones debidas al conflicto armado colombianoMinCienciasAdministradoresInvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.unal.edu.co/bitstream/unal/82171/1/license.txt8a4605be74aa9ea9d79846c1fba20a33MD51ORIGINAL1098707202.2022.pdf1098707202.2022.pdfTesis de Maestría en Ingeniería - Automatización Industrialapplication/pdf1158798https://repositorio.unal.edu.co/bitstream/unal/82171/2/1098707202.2022.pdffc6d86c2d2b7a67d1016490ecfc0f78aMD52THUMBNAIL1098707202.2022.pdf.jpg1098707202.2022.pdf.jpgGenerated Thumbnailimage/jpeg4692https://repositorio.unal.edu.co/bitstream/unal/82171/3/1098707202.2022.pdf.jpg0b24439d90e3b1d81d853953b457eb89MD53unal/82171oai:repositorio.unal.edu.co:unal/821712023-08-08 23:04:05.512Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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 |