Classification techniques for imaginary speech brain signal through spatial functional data

ilustraciones (principalmente a color), diagramas

Autores:: Bejarano Salcedo, Valeria

Tipo de recurso:

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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network_acronym_str	UNACIONAL2
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repository_id_str
dc.title.eng.fl_str_mv	Classification techniques for imaginary speech brain signal through spatial functional data
dc.title.translated.spa.fl_str_mv	Técnicas de clasificación para discurso imaginario por señales del cerebro a través de datos espaciales funcionales
title	Classification techniques for imaginary speech brain signal through spatial functional data
spellingShingle	Classification techniques for imaginary speech brain signal through spatial functional data 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas 610 - Medicina y salud Proceso de imágenes Image processing Electroencefalografía Electroencephalography Análisis espacial (Estadística) Spatial analysis (statistics) EEG spatial functional data kriging image classification datos funcionales espaciales clasificación de imágenes EEG kriging Krigeaje kriging
title_short	Classification techniques for imaginary speech brain signal through spatial functional data
title_full	Classification techniques for imaginary speech brain signal through spatial functional data
title_fullStr	Classification techniques for imaginary speech brain signal through spatial functional data
title_full_unstemmed	Classification techniques for imaginary speech brain signal through spatial functional data
title_sort	Classification techniques for imaginary speech brain signal through spatial functional data
dc.creator.fl_str_mv	Bejarano Salcedo, Valeria
dc.contributor.advisor.spa.fl_str_mv	Bohorquez Castañeda, Martha Patricia
dc.contributor.author.spa.fl_str_mv	Bejarano Salcedo, Valeria
dc.contributor.orcid.spa.fl_str_mv	Bejarano Salcedo, Valeria [0000-0002-8975-2641]
dc.contributor.cvlac.spa.fl_str_mv	Bejarano Salcedo, Valeria [0001764581]
dc.subject.ddc.spa.fl_str_mv	510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas 610 - Medicina y salud
topic	510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas 610 - Medicina y salud Proceso de imágenes Image processing Electroencefalografía Electroencephalography Análisis espacial (Estadística) Spatial analysis (statistics) EEG spatial functional data kriging image classification datos funcionales espaciales clasificación de imágenes EEG kriging Krigeaje kriging
dc.subject.lcc.spa.fl_str_mv	Proceso de imágenes
dc.subject.lcc.eng.fl_str_mv	Image processing
dc.subject.decs.spa.fl_str_mv	Electroencefalografía
dc.subject.decs.eng.fl_str_mv	Electroencephalography
dc.subject.lemb.spa.fl_str_mv	Análisis espacial (Estadística)
dc.subject.lemb.eng.fl_str_mv	Spatial analysis (statistics)
dc.subject.proposal.eng.fl_str_mv	EEG spatial functional data kriging image classification
dc.subject.proposal.spa.fl_str_mv	datos funcionales espaciales clasificación de imágenes EEG kriging
dc.subject.wikidata.spa.fl_str_mv	Krigeaje
dc.subject.wikidata.eng.fl_str_mv	kriging
description	ilustraciones (principalmente a color), diagramas
publishDate	2023
dc.date.issued.none.fl_str_mv	2023
dc.date.accessioned.none.fl_str_mv	2024-01-15T14:46:46Z
dc.date.available.none.fl_str_mv	2024-01-15T14:46:46Z
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/85267
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/85267 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.references.spa.fl_str_mv	Abiri, R., Borhani, S., Sellers, E. W., Jiang, Y., and Zhao, X. (2019). A comprehensive review of eeg-based brain-computer interface paradigms. Journal of neural engineering, 16(1):011001. Alderson-Day, B. and Fernyhough, C. (2015). Inner speech: development, cognitive functions, phenomenology, and neurobiology. Psychological bulletin, 141(5):931. Aneiros, G., Cao, R., Fraiman, R., Genest, C., and Vieu, P. (2019). Recent advances in functional data analysis and high-dimensional statistics. Journal of Multivariate Analysis, 170:3-9. Aneiros, G., Horová, I., Husková, M., and Vieu, P. (2022). On functional data analysis and related topics. Journal of Multivariate Analysis, 189:104861. Arnal, L. H., Poeppel, D., and Giraud, A.-L. (2016). A neurophysiological perspective on speech processing in "the neurobiology of language". In Neurobiology of language, pages 463-478. Elsevier. Baíllo, A., Cuevas, A., and Fraiman, R. (2011). Classi fication methods for functional data. The Oxford handbook of functional data analysis. Berlinet, A., Biau, G., and Rouviere, L. (2008). Functional supervised classi fication with wavelets. In Annales de l'ISUP, volume 52, page 19. Bijsterbosch, J. D., Woolrich, M. W., Glasser, M. F., Robinson, E. C., Beckmann, C. F., Van Essen, D. C., Harrison, S. J., and Smith, S. M. (2018). The relationship between spatial con figuration and functional connectivity of brain regions. elife, 7:e32992. Bishop, C. M. and Nasrabadi, N. M. (2006). Pattern recognition and machine learning, volume 4. Springer. Bohorquez, M., Giraldo, R., and Mateu, J. (2016). Optimal sampling for spatial prediction of functional data. Statistical Methods & Applications, 25(1):39-54. Bohorquez, M., Giraldo, R., and Mateu, J. (2017). Multivariate functional random fields: prediction and optimal sampling. Stochastic Environmental Research and Risk Assess- ment, 31(1):53-70. Bohorquez, M., Giraldo, R., and Mateu, J. (2022). Spatial prediction and optimal sampling for multivariate functional random fields. Geostatistical Functional Data Analysis, pages 329-349. Bosq, D. (2000). Linear processes in function spaces: theory and applications, volume 149. Springer Science & Business Media. Breiman, L. (2017). Classi fication and regression trees. Routledge. Bulárka, S. and Gontean, A. (2016). Brain-computer interface review. In 2016 12th IEEE International Symposium on Electronics and Telecommunications (ISETC), pages 219- 222. IEEE. Buzsáki, G. and da Silva, F. L. (2012). High frequency oscillations in the intact brain. Progress in neurobiology, 98(3):241-249. Chamroukhi, F. and Nguyen, H. D. (2019). Model-based clustering and classi fication of functional data. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(4):e1298. Chiles, J.-P. and Del ner, P. (2009). Geostatistics: modeling spatial uncertainty, volume 497. John Wiley & Sons. Chollet, F. (2021). Deep learning with Python. Simon and Schuster. Cuevas, A. (2014). A partial overview of the theory of statistics with functional data. Journal of Statistical Planning and Inference, 147:1-23. DaSalla, C. S., Kambara, H., Sato, M., and Koike, Y. (2009). Single-trial classifi cation of vowel speech imagery using common spatial patterns. Neural networks, 22(9):1334-1339. Dietrich, D., Lang, R., Bruckner, D., Fodor, G., and Müller, B. (2010). Limitations, possibilities and implications of brain-computer interfaces. In 3rd International Conference on Human System Interaction, pages 722-726. Dubuc, B. (8 de mayo de 2023). MODELS OF SPOKEN AND WRITTEN LANGUAGE FUNCTIONS IN THE BRAIN. https://thebrain.mcgill.ca/flash/d/d_10/d_10_ cr/d_10_cr_lan/d_10_cr_lan.html#2. D'Zmura, M., Deng, S., Lappas, T., Thorpe, S., and Srinivasan, R. (2009). Toward eeg sensing of imagined speech. In International Conference on Human-Computer Interaction, pages 40-48. Springer. Elías Fernández, A. (2020). Depth-based method for functional data analysis. Febrero-Bande, M., González-Manteiga, W., Prallon, B., and Saporito, Y. F. (2022). Functional classi fication of bitcoin addresses. arXiv preprint arXiv:2202.12019. Ferraty, F. (2011). Recent advances in functional data analysis and related topics. Ferraty, F. and Vieu, P. (2006). Nonparametric functional data analysis: theory and practice, volume 76. Springer. Ghane, P. (2015). Silent speech recognition in EEG-based Brain Computer Interface. Purdue University. Giraldo, R., Delicado, P., and Mateu, J. (2011). Ordinary kriging for function-valued spatial data. Environmental and ecological statistics, 18(3):411-426. Goulard, M. and Voltz, M. (1993). Geostatistical interpolation of curves: a case study in soil science. In Geostatistics Tróia'92, pages 805-816. Springer. Guo, X., Kurtek, S., and Bharath, K. (2022). Variograms for kriging and clustering of spatial functional data with phase variation. Spatial Statistics, page 100687. Harezlak, J., Ruppert, D., and Wand, M. P. (2018). Semiparametric regression with R. Springer. Hochberg, L. R. and Donoghue, J. P. (2006). Sensors for brain-computer interfaces. IEEE Engineering in Medicine and Biology Magazine, 25(5):32-38. Horváth, L. and Kokoszka, P. (2012). Inference for functional data with applications, volume 200. Springer Science & Business Media. Hsing, T. and Eubank, R. (2015). Theoretical foundations of functional data analysis, with an introduction to linear operators. John Wiley & Sons, Chichester- United Kingdom. Jacques, J. and Preda, C. (2014). Functional data clustering: a survey. Advances in Data Analysis and Classi fication, 8(3):231-255. Kumar, S., Khan, Z., and Jain, A. (2012). A review of content based image classifi cation using machine learning approach. International Journal of Advanced Computer Research, 2(3):55. Li, Y., Qiu, Y., and Xu, Y. (2022). From multivariate to functional data analysis: Fundamentals, recent developments, and emerging areas. Journal of Multivariate Analysis, 188:104806. López-Pintado, S. and Romo, J. (2006). Depth-based classi fication for functional data. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 72:103. Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., and Yger, F. (2018). A review of classi fication algorithms for eeg-based brain{computer interfaces: a 10 year update. Journal of neural engineering, 15(3):031005. Loussaief, S. and Abdelkrim, A. (2016). Machine learning framework for image classi fication. In 2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), pages 58-61. IEEE. Manjrekar, O. N. and Dudukovic, M. P. (2019). Identifi cation of flow regime in a bubble column reactor with a combination of optical probe data and machine learning technique. Chemical Engineering Science: X, 2:100023. Mardiyyah, A. (2020). The support vector machine: Basic concept. https://medium.com/ swlh/the-support-vector-machine-basic-concept-a5106bd3cc5f. Markello, R. D., Hansen, J. Y., Liu, Z.-Q., Bazinet, V., Sha ei, G., Suárez, L. E., Blostein, N., Seidlitz, J., Baillet, S., Satterthwaite, T. D., et al. (2022). Neuromaps: structural and functional interpretation of brain maps. Nature Methods, 19(11):1472-1479. Marsland, S. (2015). Machine learning: an algorithmic perspective. CRC press. Martin-Barragan, B., Lillo, R., and Romo, J. (2014). Interpretable support vector machines for functional data. European Journal of Operational Research, 232(1):146-155. Mesulam, M. (2011). Brain mapping. Mitra, P. P. and Pesaran, B. (1999). Analysis of dynamic brain imaging data. Biophysical journal, 76(2):691-708. Mucherino, A., Papajorgji, P. J., Pardalos, P. M., Mucherino, A., Papajorgji, P. J., and Pardalos, P. M. (2009). K-nearest neighbor classi fication. Data mining in agriculture, pages 83-106. Nath, S. S., Mishra, G., Kar, J., Chakraborty, S., and Dey, N. (2014). A survey of image classi fication methods and techniques. In 2014 International conference on control, instrumentation, communication and computational technologies (ICCICCT), pages 554-557. IEEE. Nicolas-Alonso, L. F. and Gomez-Gil, J. (2012). Brain computer interfaces, a review. sensors, 12(2):1211-1279. Olokodana, I. L., Mohanty, S. P., and Kougianos, E. (2020). Krig-detect: Exploring alternative kriging methods for real-time seizure detection from eeg signals. In 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), pages 1-6. IEEE. Oshinubi, K., Ibrahim, F., Rachdi, M., and Demongeot, J. (2022). Functional data analysis: Application to daily observation of covid-19 prevalence in france. AIMS Mathematics, 7(4):5347-5385. Patil, P. G. and Turner, D. A. (2008). The development of brain-machine interface neuroprosthetic devices. Neurotherapeutics, 5:137-146. Pebesma, E. J. (2001). Gstat user's manual. Dept. of Physical Geography, Utrecht University, Utrecht, The Netherlands. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830. Popescu, M. C. and Sasu, L. M. (2014). Feature extraction, feature selection and machine learning for image classi fication: A case study. In 2014 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM), pages 968-973. IEEE. Porbadnigk, A., Wester, M., and Calliess, T. S. J.-p. (2009). Eeg-based speech recognition impact of temporal effects. Professor Dave Explains (2020). The Brain and Language. https://www.youtube.com/ watch?v=zj0yud4wv74. [Video file]. Ramsay, J. and Silverman, B. (2005). Functional Data Analysis. Springer, New York. Rathi, V. P. and Palani, S. (2012). Brain tumor mri image classifi cation with feature selection and extraction using linear discriminant analysis. arXiv preprint arXiv:1208.2128. Roehm, D., Klimesch, W., Haider, H., and Doppelmayr, M. (2001). The role of theta and alpha oscillations for language comprehension in the human electroencephalogram. Neuroscience letters, 310(2-3):137-140. Rossi, F. and Villa, N. (2008). Recent advances in the use of svm for functional data classi fication. Functional and operatorial statistics, pages 273-280. Sarmiento, L. C., Villamizar, S., López, O., Collazos, A. C., Sarmiento, J., and Rodríguez, J. B. (2021). Recognition of eeg signals from imagined vowels using deep learning methods. Sensors, 21(19):6503. Schultz, T., Wand, M., Hueber, T., Krusienski, D. J., Herff, C., and Brumberg, J. S. (2017). Biosignal-based spoken communication: A survey. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 25(12):2257-2271. Secchi, P., Vantini, S., and Vitelli, V. (2011). Spatial clustering of functional data. In Recent Advances in Functional Data Analysis and Related Topics, pages 283-289. Springer. Sguera, C., Galeano, P., and Lillo, R. (2014). Spatial depth-based classifi cation for functional data. Test, 23(4):725-750. Shangguan, P., Qiu, T., Liu, T., Zou, S., Liu, Z., and Zhang, S. (2020). Feature extraction of eeg signals based on functional data analysis and its application to recognition of driver fatigue state. Physiological Measurement, 41(12):125004. Tamuly, S., Jyotsna, C., and Amudha, J. (2020). Deep learning model for image classifi cation. In Computational Vision and Bio-Inspired Computing: ICCVBIC 2019, pages 312-320. Springer. Vandewalle, V., Preda, C., and Dabo-Niang, S. (2022). Clustering spatial functional data. Geostatistical Functional Data Analysis, pages 155-174. Wang, J.-L., Chiou, J.-M., and Müller, H.-G. (2016). Functional data analysis. Annual Review of Statistics and its application, 3:257-295. Wang, P., Fan, E., and Wang, P. (2021). Comparative analysis of image classi fication algorithms based on traditional machine learning and deep learning. Pattern Recognition Letters, 141:61-67. Wang, W., Liang, D., Chen, Q., Iwamoto, Y., Han, X.-H., Zhang, Q., Hu, H., Lin, L., and Chen, Y.-W. (2020). Medical image classifi cation using deep learning. In Deep learning in healthcare, pages 33-51. Springer. Wester, M. (2006). Unspoken speech-speech recognition based on electroencephalography. Master's Thesis, Universitat Karlsruhe (TH). Wu, H. and Li, Y.-F. (2022). Clustering spatially correlated functional data with multiple scalar covariates. IEEE Transactions on Neural Networks and Learning Systems. Yi, Y., Billor, N., Liang, M., Cao, X., Ekstrom, A., and Zheng, J. (2022). Classi cation of eeg signals: An interpretable approach using functional data analysis. Journal of Neuroscience Methods, 376:109609. Zhang, J.-T. and Zhu, T. (2022). A new k-nearest neighbors classi fier for functional data. Statistics and Its Interface, 15(2):247-260.
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv	Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial 4.0 Internacional http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2
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dc.format.extent.spa.fl_str_mv	ix, 55 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ciencias - Maestría en Ciencias - Estadística
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Bohorquez Castañeda, Martha Patricia2e2e02de049d58b3081d25a3e7e00efdBejarano Salcedo, Valeria091673edfab0feb7918856e9b6305bbcBejarano Salcedo, Valeria [0000-0002-8975-2641]Bejarano Salcedo, Valeria [0001764581]2024-01-15T14:46:46Z2024-01-15T14:46:46Z2023https://repositorio.unal.edu.co/handle/unal/85267Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones (principalmente a color), diagramasThe present work aims to classify the thought of the five Spanish vowels measured by electroencephalograms (EEG) of 21 electrodes around the Broca's area of the brain of 23 individuals. This was addressed by the framework of spatial functional data, considering each EEG a continuous curve in L2 and performing functional kriging, several images were constructed to apply classification techniques of machine and deep learning. Finally, both classification routines on average achieve more than 91\% precision for each individual, considering that each individual should have its own classification mechanism. (Texto tomado de la fuente)El presente trabajo tiene como objetivo clasificar el pensamiento de las cinco vocales del idioma español medidas a través de electroencefalogramas en 21 electrodos alrededor del área de Broca del cerebro en 23 individuos. Para esto se empleó el marco de los datos espaciales funcionales, considerando cada medición EEG una curva continua en L2 y realizando kriging funcional, se construyeron varias imágenes para aplicar técnicas de clasificación de aprendizaje de máquina y profundo. Finalmente, ambas rutinas de clasificación en promedio lograron una precisión de más del 91% para cada individuo, hay que tener en cuenta que cada individuo debe contar con su propio mecanismo de clasificación.MaestríaMaestría en Ciencias - EstadísticaDatos espaciales funcionalesix, 55 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas610 - Medicina y saludProceso de imágenesImage processingElectroencefalografíaElectroencephalographyAnálisis espacial (Estadística)Spatial analysis (statistics)EEGspatial functional datakrigingimage classificationdatos funcionales espacialesclasificación de imágenesEEGkrigingKrigeajekrigingClassification techniques for imaginary speech brain signal through spatial functional dataTécnicas de clasificación para discurso imaginario por señales del cerebro a través de datos espaciales funcionalesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAbiri, R., Borhani, S., Sellers, E. W., Jiang, Y., and Zhao, X. (2019). A comprehensive review of eeg-based brain-computer interface paradigms. Journal of neural engineering, 16(1):011001.Alderson-Day, B. and Fernyhough, C. (2015). Inner speech: development, cognitive functions, phenomenology, and neurobiology. Psychological bulletin, 141(5):931.Aneiros, G., Cao, R., Fraiman, R., Genest, C., and Vieu, P. (2019). Recent advances in functional data analysis and high-dimensional statistics. Journal of Multivariate Analysis, 170:3-9.Aneiros, G., Horová, I., Husková, M., and Vieu, P. (2022). On functional data analysis and related topics. Journal of Multivariate Analysis, 189:104861.Arnal, L. H., Poeppel, D., and Giraud, A.-L. (2016). A neurophysiological perspective on speech processing in "the neurobiology of language". In Neurobiology of language, pages 463-478. Elsevier.Baíllo, A., Cuevas, A., and Fraiman, R. (2011). Classi fication methods for functional data. The Oxford handbook of functional data analysis.Berlinet, A., Biau, G., and Rouviere, L. (2008). Functional supervised classi fication with wavelets. In Annales de l'ISUP, volume 52, page 19.Bijsterbosch, J. D., Woolrich, M. W., Glasser, M. F., Robinson, E. C., Beckmann, C. F., Van Essen, D. C., Harrison, S. J., and Smith, S. M. (2018). The relationship between spatial con figuration and functional connectivity of brain regions. elife, 7:e32992.Bishop, C. M. and Nasrabadi, N. M. (2006). Pattern recognition and machine learning, volume 4. Springer.Bohorquez, M., Giraldo, R., and Mateu, J. (2016). Optimal sampling for spatial prediction of functional data. Statistical Methods & Applications, 25(1):39-54.Bohorquez, M., Giraldo, R., and Mateu, J. (2017). Multivariate functional random fields: prediction and optimal sampling. Stochastic Environmental Research and Risk Assess- ment, 31(1):53-70.Bohorquez, M., Giraldo, R., and Mateu, J. (2022). Spatial prediction and optimal sampling for multivariate functional random fields. Geostatistical Functional Data Analysis, pages 329-349.Bosq, D. (2000). Linear processes in function spaces: theory and applications, volume 149. Springer Science & Business Media.Breiman, L. (2017). Classi fication and regression trees. Routledge.Bulárka, S. and Gontean, A. (2016). Brain-computer interface review. In 2016 12th IEEE International Symposium on Electronics and Telecommunications (ISETC), pages 219- 222. IEEE.Buzsáki, G. and da Silva, F. L. (2012). High frequency oscillations in the intact brain. Progress in neurobiology, 98(3):241-249.Chamroukhi, F. and Nguyen, H. D. (2019). Model-based clustering and classi fication of functional data. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(4):e1298.Chiles, J.-P. and Del ner, P. (2009). Geostatistics: modeling spatial uncertainty, volume 497. John Wiley & Sons.Chollet, F. (2021). Deep learning with Python. Simon and Schuster.Cuevas, A. (2014). A partial overview of the theory of statistics with functional data. Journal of Statistical Planning and Inference, 147:1-23.DaSalla, C. S., Kambara, H., Sato, M., and Koike, Y. (2009). Single-trial classifi cation of vowel speech imagery using common spatial patterns. Neural networks, 22(9):1334-1339.Dietrich, D., Lang, R., Bruckner, D., Fodor, G., and Müller, B. (2010). Limitations, possibilities and implications of brain-computer interfaces. In 3rd International Conference on Human System Interaction, pages 722-726.Dubuc, B. (8 de mayo de 2023). MODELS OF SPOKEN AND WRITTEN LANGUAGE FUNCTIONS IN THE BRAIN. https://thebrain.mcgill.ca/flash/d/d_10/d_10_ cr/d_10_cr_lan/d_10_cr_lan.html#2.D'Zmura, M., Deng, S., Lappas, T., Thorpe, S., and Srinivasan, R. (2009). Toward eeg sensing of imagined speech. In International Conference on Human-Computer Interaction, pages 40-48. Springer.Elías Fernández, A. (2020). Depth-based method for functional data analysis.Febrero-Bande, M., González-Manteiga, W., Prallon, B., and Saporito, Y. F. (2022). Functional classi fication of bitcoin addresses. arXiv preprint arXiv:2202.12019.Ferraty, F. (2011). Recent advances in functional data analysis and related topics.Ferraty, F. and Vieu, P. (2006). Nonparametric functional data analysis: theory and practice, volume 76. Springer.Ghane, P. (2015). Silent speech recognition in EEG-based Brain Computer Interface. Purdue University.Giraldo, R., Delicado, P., and Mateu, J. (2011). Ordinary kriging for function-valued spatial data. Environmental and ecological statistics, 18(3):411-426.Goulard, M. and Voltz, M. (1993). Geostatistical interpolation of curves: a case study in soil science. In Geostatistics Tróia'92, pages 805-816. Springer.Guo, X., Kurtek, S., and Bharath, K. (2022). Variograms for kriging and clustering of spatial functional data with phase variation. Spatial Statistics, page 100687.Harezlak, J., Ruppert, D., and Wand, M. P. (2018). Semiparametric regression with R. Springer.Hochberg, L. R. and Donoghue, J. P. (2006). Sensors for brain-computer interfaces. IEEE Engineering in Medicine and Biology Magazine, 25(5):32-38.Horváth, L. and Kokoszka, P. (2012). Inference for functional data with applications, volume 200. Springer Science & Business Media.Hsing, T. and Eubank, R. (2015). Theoretical foundations of functional data analysis, with an introduction to linear operators. John Wiley & Sons, Chichester- United Kingdom.Jacques, J. and Preda, C. (2014). Functional data clustering: a survey. Advances in Data Analysis and Classi fication, 8(3):231-255.Kumar, S., Khan, Z., and Jain, A. (2012). A review of content based image classifi cation using machine learning approach. International Journal of Advanced Computer Research, 2(3):55.Li, Y., Qiu, Y., and Xu, Y. (2022). From multivariate to functional data analysis: Fundamentals, recent developments, and emerging areas. Journal of Multivariate Analysis, 188:104806.López-Pintado, S. and Romo, J. (2006). Depth-based classi fication for functional data. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 72:103.Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., and Yger, F. (2018). A review of classi fication algorithms for eeg-based brain{computer interfaces: a 10 year update. Journal of neural engineering, 15(3):031005.Loussaief, S. and Abdelkrim, A. (2016). Machine learning framework for image classi fication. In 2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), pages 58-61. IEEE.Manjrekar, O. N. and Dudukovic, M. P. (2019). Identifi cation of flow regime in a bubble column reactor with a combination of optical probe data and machine learning technique. Chemical Engineering Science: X, 2:100023.Mardiyyah, A. (2020). The support vector machine: Basic concept. https://medium.com/ swlh/the-support-vector-machine-basic-concept-a5106bd3cc5f.Markello, R. D., Hansen, J. Y., Liu, Z.-Q., Bazinet, V., Sha ei, G., Suárez, L. E., Blostein, N., Seidlitz, J., Baillet, S., Satterthwaite, T. D., et al. (2022). Neuromaps: structural and functional interpretation of brain maps. Nature Methods, 19(11):1472-1479.Marsland, S. (2015). Machine learning: an algorithmic perspective. CRC press.Martin-Barragan, B., Lillo, R., and Romo, J. (2014). Interpretable support vector machines for functional data. European Journal of Operational Research, 232(1):146-155.Mesulam, M. (2011). Brain mapping.Mitra, P. P. and Pesaran, B. (1999). Analysis of dynamic brain imaging data. Biophysical journal, 76(2):691-708.Mucherino, A., Papajorgji, P. J., Pardalos, P. M., Mucherino, A., Papajorgji, P. J., and Pardalos, P. M. (2009). K-nearest neighbor classi fication. Data mining in agriculture, pages 83-106.Nath, S. S., Mishra, G., Kar, J., Chakraborty, S., and Dey, N. (2014). A survey of image classi fication methods and techniques. In 2014 International conference on control, instrumentation, communication and computational technologies (ICCICCT), pages 554-557. IEEE.Nicolas-Alonso, L. F. and Gomez-Gil, J. (2012). Brain computer interfaces, a review. sensors, 12(2):1211-1279.Olokodana, I. L., Mohanty, S. P., and Kougianos, E. (2020). Krig-detect: Exploring alternative kriging methods for real-time seizure detection from eeg signals. In 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), pages 1-6. IEEE.Oshinubi, K., Ibrahim, F., Rachdi, M., and Demongeot, J. (2022). Functional data analysis: Application to daily observation of covid-19 prevalence in france. AIMS Mathematics, 7(4):5347-5385.Patil, P. G. and Turner, D. A. (2008). The development of brain-machine interface neuroprosthetic devices. Neurotherapeutics, 5:137-146.Pebesma, E. J. (2001). Gstat user's manual. Dept. of Physical Geography, Utrecht University, Utrecht, The Netherlands.Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830.Popescu, M. C. and Sasu, L. M. (2014). Feature extraction, feature selection and machine learning for image classi fication: A case study. In 2014 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM), pages 968-973. IEEE.Porbadnigk, A., Wester, M., and Calliess, T. S. J.-p. (2009). Eeg-based speech recognition impact of temporal effects.Professor Dave Explains (2020). The Brain and Language. https://www.youtube.com/ watch?v=zj0yud4wv74. [Video file].Ramsay, J. and Silverman, B. (2005). Functional Data Analysis. Springer, New York.Rathi, V. P. and Palani, S. (2012). Brain tumor mri image classifi cation with feature selection and extraction using linear discriminant analysis. arXiv preprint arXiv:1208.2128.Roehm, D., Klimesch, W., Haider, H., and Doppelmayr, M. (2001). The role of theta and alpha oscillations for language comprehension in the human electroencephalogram. Neuroscience letters, 310(2-3):137-140.Rossi, F. and Villa, N. (2008). Recent advances in the use of svm for functional data classi fication. Functional and operatorial statistics, pages 273-280.Sarmiento, L. C., Villamizar, S., López, O., Collazos, A. C., Sarmiento, J., and Rodríguez, J. B. (2021). Recognition of eeg signals from imagined vowels using deep learning methods. Sensors, 21(19):6503.Schultz, T., Wand, M., Hueber, T., Krusienski, D. J., Herff, C., and Brumberg, J. S. (2017). Biosignal-based spoken communication: A survey. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 25(12):2257-2271.Secchi, P., Vantini, S., and Vitelli, V. (2011). Spatial clustering of functional data. In Recent Advances in Functional Data Analysis and Related Topics, pages 283-289. Springer.Sguera, C., Galeano, P., and Lillo, R. (2014). Spatial depth-based classifi cation for functional data. Test, 23(4):725-750.Shangguan, P., Qiu, T., Liu, T., Zou, S., Liu, Z., and Zhang, S. (2020). Feature extraction of eeg signals based on functional data analysis and its application to recognition of driver fatigue state. Physiological Measurement, 41(12):125004.Tamuly, S., Jyotsna, C., and Amudha, J. (2020). Deep learning model for image classifi cation. In Computational Vision and Bio-Inspired Computing: ICCVBIC 2019, pages 312-320. Springer.Vandewalle, V., Preda, C., and Dabo-Niang, S. (2022). Clustering spatial functional data. Geostatistical Functional Data Analysis, pages 155-174.Wang, J.-L., Chiou, J.-M., and Müller, H.-G. (2016). Functional data analysis. Annual Review of Statistics and its application, 3:257-295.Wang, P., Fan, E., and Wang, P. (2021). Comparative analysis of image classi fication algorithms based on traditional machine learning and deep learning. Pattern Recognition Letters, 141:61-67.Wang, W., Liang, D., Chen, Q., Iwamoto, Y., Han, X.-H., Zhang, Q., Hu, H., Lin, L., and Chen, Y.-W. (2020). Medical image classifi cation using deep learning. In Deep learning in healthcare, pages 33-51. Springer.Wester, M. (2006). Unspoken speech-speech recognition based on electroencephalography. Master's Thesis, Universitat Karlsruhe (TH).Wu, H. and Li, Y.-F. (2022). Clustering spatially correlated functional data with multiple scalar covariates. IEEE Transactions on Neural Networks and Learning Systems.Yi, Y., Billor, N., Liang, M., Cao, X., Ekstrom, A., and Zheng, J. (2022). Classi cation of eeg signals: An interpretable approach using functional data analysis. Journal of Neuroscience Methods, 376:109609.Zhang, J.-T. and Zhu, T. (2022). A new k-nearest neighbors classi fier for functional data. Statistics and Its Interface, 15(2):247-260.EstudiantesInvestigadoresMaestrosPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/85267/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINALTesis de Maestría en Ciencias - Estadística.pdfTesis de Maestría en Ciencias - Estadística.pdfapplication/pdf59712036https://repositorio.unal.edu.co/bitstream/unal/85267/2/Tesis%20de%20Maestr%c3%ada%20en%20Ciencias%20-%20Estad%c3%adstica.pdff6b66e84859eb0e6ed51e2ffc24fa95eMD52THUMBNAILTesis de Maestría en Ciencias - Estadística.pdf.jpgTesis de Maestría en Ciencias - Estadística.pdf.jpgGenerated Thumbnailimage/jpeg3824https://repositorio.unal.edu.co/bitstream/unal/85267/3/Tesis%20de%20Maestr%c3%ada%20en%20Ciencias%20-%20Estad%c3%adstica.pdf.jpg0332064a22e4392ac2cb32cf661465c7MD53unal/85267oai:repositorio.unal.edu.co:unal/852672024-08-20 23:10:52.24Repositorio Institucional Universidad Nacional de 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Classification techniques for imaginary speech brain signal through spatial functional data

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