A machine learning framework for EEG-based BCI-MI skill prediction with preserved explainability

graficas, tablas

Autores:
Caicedo Acosta, Julian Camilo
Tipo de recurso:
Doctoral thesis
Fecha de publicación:
2024
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
eng
OAI Identifier:
oai:repositorio.unal.edu.co:unal/86900
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/86900
https://repositorio.unal.edu.co/
Palabra clave:
000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación
BCI-illiteracy
EEG
Brain computer interface
Motor imagery
Neurophysiological indicators
MI performance prediction
MI classification
Improvement BCI-illiterate subjects
Interfaz Cerebro-Computadora
Imaginación motora
Indicadores neurofisiológicos
Predicción de rendimiento en MI
Clasificación en MI
Mejoramiento de los sujetos BCIilliterate
Neurociencia computacional
Interfaces cerebro-computadora
Rights
openAccess
License
Reconocimiento 4.0 Internacional
id UNACIONAL2_ad8912c7013644141e3673c21c8757c7
oai_identifier_str oai:repositorio.unal.edu.co:unal/86900
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.eng.fl_str_mv A machine learning framework for EEG-based BCI-MI skill prediction with preserved explainability
dc.title.translated.spa.fl_str_mv Un marco de aprendizaje automático para la predicción de habilidades BCI-MI basadas en EEG con interpretabilidad preservada
title A machine learning framework for EEG-based BCI-MI skill prediction with preserved explainability
spellingShingle A machine learning framework for EEG-based BCI-MI skill prediction with preserved explainability
000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación
BCI-illiteracy
EEG
Brain computer interface
Motor imagery
Neurophysiological indicators
MI performance prediction
MI classification
Improvement BCI-illiterate subjects
Interfaz Cerebro-Computadora
Imaginación motora
Indicadores neurofisiológicos
Predicción de rendimiento en MI
Clasificación en MI
Mejoramiento de los sujetos BCIilliterate
Neurociencia computacional
Interfaces cerebro-computadora
title_short A machine learning framework for EEG-based BCI-MI skill prediction with preserved explainability
title_full A machine learning framework for EEG-based BCI-MI skill prediction with preserved explainability
title_fullStr A machine learning framework for EEG-based BCI-MI skill prediction with preserved explainability
title_full_unstemmed A machine learning framework for EEG-based BCI-MI skill prediction with preserved explainability
title_sort A machine learning framework for EEG-based BCI-MI skill prediction with preserved explainability
dc.creator.fl_str_mv Caicedo Acosta, Julian Camilo
dc.contributor.advisor.none.fl_str_mv Castellanos Dominguez, Cesar German
Alvarez Meza, Andres Marino
dc.contributor.author.none.fl_str_mv Caicedo Acosta, Julian Camilo
dc.contributor.researchgroup.spa.fl_str_mv Grupo de Control y Procesamiento Digital de Señales
dc.subject.ddc.spa.fl_str_mv 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación
topic 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación
BCI-illiteracy
EEG
Brain computer interface
Motor imagery
Neurophysiological indicators
MI performance prediction
MI classification
Improvement BCI-illiterate subjects
Interfaz Cerebro-Computadora
Imaginación motora
Indicadores neurofisiológicos
Predicción de rendimiento en MI
Clasificación en MI
Mejoramiento de los sujetos BCIilliterate
Neurociencia computacional
Interfaces cerebro-computadora
dc.subject.proposal.eng.fl_str_mv BCI-illiteracy
EEG
Brain computer interface
Motor imagery
Neurophysiological indicators
MI performance prediction
MI classification
Improvement BCI-illiterate subjects
dc.subject.proposal.spa.fl_str_mv Interfaz Cerebro-Computadora
Imaginación motora
Indicadores neurofisiológicos
Predicción de rendimiento en MI
Clasificación en MI
Mejoramiento de los sujetos BCIilliterate
dc.subject.unesco.none.fl_str_mv Neurociencia computacional
Interfaces cerebro-computadora
description graficas, tablas
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-10-07T14:07:48Z
dc.date.available.none.fl_str_mv 2024-10-07T14:07:48Z
dc.date.issued.none.fl_str_mv 2024
dc.type.spa.fl_str_mv Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv Text
format http://purl.org/coar/resource_type/c_db06
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/86900
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/86900
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
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dc.publisher.program.spa.fl_str_mv Manizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - Automática
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spelling Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Castellanos Dominguez, Cesar German6d037451703825ac65848e0563c65fd2Alvarez Meza, Andres Marino5154abb266e67961a71f8dc28e883cbfCaicedo Acosta, Julian Camiloa41c9a88d8d0ddbc1448db4811350bd4600Grupo de Control y Procesamiento Digital de Señales2024-10-07T14:07:48Z2024-10-07T14:07:48Z2024https://repositorio.unal.edu.co/handle/unal/86900Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/graficas, tablasThe understanding of brain functioning, including its structure and the dynamic networks generated under different situations, has been one of the most challenging research topics in recent years. In this sense, brain- computer interfaces (BCI) have become the main systems to acquire and process brain signals, allowing the development of increasingly specialized techniques like machine learning and artificial intelligence methods in order to replace, restore, enhance, supply, and improve brain functionality. Thus, BCI systems may be a promissory tool for several fields, including health, rehabilitation, education, marketing, and gaming, among others. Besides, BCI development is a continuously growing field of study not only in research but also in the business context, with a market that projects more than four hundred million USD by 2029. However, despite advances in building BCI systems, around 30% of BCI users are unable to effectively manage the device due to multiple factors such as intra- and inter-subject variability, overfitting, and small datasets. This issue is known as the ‘BCI illiteracy phenomenon’. Since electroencephalography (EEG) is the most used acquisition method due to its high temporal resolution, portability, and low cost compared with the other techniques, in this work we study the causes of the illiteracy phenomenon by predicting the user performance during motor imagery (MI) tasks. In this sense, we developed a machine learning framework to improve the illiterate subject’s performance under a skill prediction scheme. First, we develop a data- driven deep learning model, termed DRN, based on extracting time-frequency neurophysiological indicators that allow coding the inter-subject variability by predicting MI-BCI performance. Besides, to address the overfitting issues in the presence of small datasets and high-dimensional representations, we also developed a functional connectivity-based Monte-Carlo dropout regularized approach by modifying the proposed DRN to extract relevant patterns from channel relationships. Last, to improve illiterate subjects’ performance, we develop a general-purpose end-to-end multi-task deep learning approach founded on an autoencoder- based regularization scheme that transfers learned knowledge from performance prediction tasks to MI classification tasks. The results obtained in this work are promising and outperform the baseline methods in both BCI-performance prediction and MI classification. Furthermore, the proposed prediction methods are able to find behavioral group patterns between subjects with similar degrees of variability, while the transfer learning techniques allow to improve the performance of illiterate subjects with information from other subjects, contributing to the generalization of BCI systems (Texto tomado de la fuente)La comprensión del funcionamiento del cerebro, incluida su estructura y las redes dinámicas generadas en diferentes situaciones, ha sido uno de los temas de investigación más difíciles de los últimos años. En este sentido, las interfaces cerebro-computador (BCI) se han convertido en los principales sistemas para adquirir y procesar señales cerebrales, permitiendo el desarrollo de técnicas cada vez más especializadas como el aprendizaje automático y los métodos de inteligencia artificial que permiten reemplazar, restaurar, mejorar, suministrar y mejorar la funcionalidad cerebral. Por lo tanto, los sistemas BCI pueden ser una herramienta prometedora para varios campos, incluyendo la salud, la rehabilitación, la educación, el marketing y el juego, entre otros. Además, el desarrollo de BCI es un campo de estudio en continuo crecimiento no sólo en investigación sino también en el contexto empresarial, con un mercado que proyecta más de cuatrocientos millones de dólares para 2029. Sin embargo, a pesar de los avances en la construcción de sistemas BCI, alrededor del 30% de los usuarios de BCI no son capaces de administrar eficazmente el dispositivo debido a múltiples factores como la variabilidad intra- e inter-sujeto, el sobreajuste y la presencia de pequeños conjuntos de datos para el entrenamiento. Este problema es conocido como el fenómeno del ‘analfabetismo del BCI’. Dado que la electroencefalografía (EEG) es el método de adquisición más utilizado debido a su alta resolución temporal, portabilidad y bajo costo en comparación con las otras técnicas, en este trabajo estudiamos las causas del fenómeno del analfabetismo previniendo el rendimiento del usuario durante las tareas de imagen motor (MI). En este sentido, hemos desarrollado un marco de aprendizaje automático para mejorar el rendimiento del sujeto analfabeto bajo un esquema de predicción de sus habilidades. En primer lugar, desarrollamos un modelo de aprendizaje profundo basado en los datos, llamado DRN, sobre la base de la extracción de indicadores neurofisiológicos de frecuencia temporal que permiten codificar la variabilidad intersubjetiva prediciendo el rendimiento del MI-BCI. Además, para abordar los problemas de sobreajuste en la presencia de pequeños conjuntos de datos y representaciones de alta dimensión, también desarrol- lamos un enfoque regularizado de Monte-Carlo dropout basado en la conectividad funcional modificando el DRN propuesto para extraer patrones relevantes de las relaciones de canales. Por último, para mejorar el rendimiento de los sujetos analfabetos, desarrollamos un enfoque de aprendizaje profundo multi-tareas de propósito general basado en un esquema de regularización usando una arquitectura de autoencoder, que transfiere los conocimientos aprendidos de las tareas de predicción de rendimiento a las tasas de clasificación de imaginación motora. Los resultados obtenidos en este trabajo son prometedores y superan los méto- dos base tanto en la predicción del desempeño del BCI como en la clasificación de la imaginación motora. Además, los métodos de predicción propuestos son capaces de encontrar patrones de comportamiento grupal entre sujetos con grados similares de variabilidad, mientras que las técnicas de transferencia de aprendizaje permiten mejorar el rendimiento de sujetos analfabetos con información de otros sujetos, contribuyendo a la generalización de los sistemas BCI.(Code 111091991908 , Hermes Code 56118 ) funded by MINCIENCIAS(Hermes Code 57414 ), funded by Universidad Nacional de ColombiaDoctoradoDoctor en IngenieríaApplied machine learningEléctrica, Electrónica, Automatización Y Telecomunicaciones.Sede Manizalesxv, 101 páginasapplication/pdfengUniversidad Nacional de ColombiaManizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - AutomáticaFacultad de Ingeniería y ArquitecturaManizales, ColombiaUniversidad Nacional de Colombia - Sede Manizales000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computaciónBCI-illiteracyEEGBrain computer interfaceMotor imageryNeurophysiological indicatorsMI performance predictionMI classificationImprovement BCI-illiterate subjectsInterfaz Cerebro-ComputadoraImaginación motoraIndicadores neurofisiológicosPredicción de rendimiento en MIClasificación en MIMejoramiento de los sujetos BCIilliterateNeurociencia computacionalInterfaces cerebro-computadoraA machine learning framework for EEG-based BCI-MI skill prediction with preserved explainabilityUn marco de aprendizaje automático para la predicción de habilidades BCI-MI basadas en EEG con interpretabilidad preservadaTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06TextAbdulkader, S. 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C.: , 2020; Eeg-based classification of natural sounds reveals specialized responses to speech and music; NeuroImage; 210: 116558.Alianzacientíficaconenfoquecomunitarioparamitigarbrechasdeatenciónymanejodetrastornos mentales y epilepsia en Colombia (ACEMATE).Sistema de integración de EEG, ECG y SpO2 para seguimiento de neonatos en unidad de cuidados intensivos del Hospital Universitario de Caldas - SES HUC.MINCIENCIASUniversidad Nacional de ColombiaBibliotecariosEstudiantesInvestigadoresPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86900/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1053847443.2024.pdf1053847443.2024.pdfTesis de Doctorado en Ingeniería - Automáticaapplication/pdf12166376https://repositorio.unal.edu.co/bitstream/unal/86900/2/1053847443.2024.pdf77e71e9434e2fe3fc228d5eed23d5a0bMD52THUMBNAIL1053847443.2024.pdf.jpg1053847443.2024.pdf.jpgGenerated 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