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A Brain-Computer Interface for labour market inclusion of people suffering severe upper-limb impairments

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dc.contributor.advisorDelisle Rodríguez, Denis
dc.contributor.advisorJiménez Hernández, Mario Fernando
dc.creatorGarcía Osorio, Juan Lucas
dc.creator.degreeProfesional en Matemáticas Aplicadas y Ciencias de la Computación
dc.creator.degreeProfesional en Matemáticas Aplicadas y Ciencias de la Computación
dc.creator.degreeLevelPregrado
dc.creator.degreetypeFull time
dc.date.accessioned2024-03-05T19:25:36Z
dc.date.available2024-03-05T19:25:36Z
dc.date.created2023-06-10
dc.descriptionLos dispositivos robóticos de asistencia, como los exoesqueletos, se utilizan en entornos laborales para favorecer la inclusión social de diversos tipos de deficiencias como, por ejemplo, las de las extremidades superiores. Los exoesqueletos robóticos pueden controlarse mediante señales electromiográficas de superficie. Sin embargo, las personas con deficiencias neurales graves y ausencia de actividad muscular residual no pueden utilizar estos sistemas basados en sEMG debido a la ausencia de actividad muscular residual. Como alternativa, se han aplicado con éxito en estas personas prótesis de mano robóticas y exoesqueletos comandados por interfaces cerebro-ordenador (BCI). El objetivo de este estudio es desarrollar una BCI de bajo coste basada en potenciales visuales evocados de estado estacionario (SSVEP) para la inclusión social, utilizando calibración no supervisada. Se propone un estimulador visual de parpadeo de bajo coste con formas geométricas para obtener órdenes cerebrales. Para clasificar los estímulos SSVEP se utilizan el análisis de correlación canónica (CCA) y la densidad espectral de potencia (PSD). Como primer paso, la BCI propuesta se probó en un juego serio desarrollado para simular el espacio de trabajo y proporcionar información al sujeto. La CCA presentó los mejores resultados de clasificación con una precisión del 71,6 ± 9,7% y una tasa de transferencia de información (ITR) de 37,6 ± 15,4 bits/min y una latencia media de 0,77 ± 0,39 s para proporcionar una salida asociada al estímulo observado por el sujeto.
dc.description.abstractRobotic assistive devices, such as exoskeletons are used in labour environments to promote social inclusion of diverse types of impairments as for example upper- limb. Robotic exoskeletons can be controlled by surface electromyography signals. However, people with severe neural impairments and absence of residual muscu- lar activity are unable of using these sEMG-based systems due to the absence of residual muscular activity. Alternatively, robotic hand prostheses and exoskeletons commanded by Brain-Computer Interfaces (BCIs) have been successfully applied in these people. This study aims to develop a low-cost steady-state visual evoked potential (SSVEP)-based BCI for social inclusion, using unsupervised calibration. A low-cost flicker visual stimulator with geometric shapes is proposed to elicit brain commands. Both Canonical Correlation Analysis (CCA) and Power Spectral Den- sity (PSD) are used to classify SSVEP stimuli. As a first step, the proposed BCI was tested in a serious game, which was developed to simulate the workspace, and provide feedback to the subject. CCA presented the best classification results with an accuracy of 71.6 ± 9.7% and an Information Transfer Rate (ITR) of 37.6 ± 15.4 bits/min and averaged latency of 0.77 ± 0.39 s to provide an output associated to the stimulus observed by the subject.
dc.format.extent82 pp
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.48713/10336_42320
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/42320
dc.language.isoeng
dc.publisherUniversidad del Rosario
dc.publisher.departmentEscuela de Ingeniería, Ciencia y Tecnología
dc.publisher.programPrograma de Matemáticas Aplicadas y Ciencias de la Computación - MACC
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
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dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR
dc.subjectInterfaz cerebro-ordenador
dc.subjectPotencial evocado visual en estado estacionario
dc.subjectInclusión social
dc.subjectJuego serio
dc.subjectDiscapacidad de miembro superior
dc.subject.keywordBrain-Computer Interface
dc.subject.keywordSteady State Visual Evoked Po- tential
dc.subject.keywordSocial inclusion
dc.subject.keywordSerious game
dc.subject.keywordUpper-limb disability
dc.titleA Brain-Computer Interface for labour market inclusion of people suffering severe upper-limb impairments
dc.title.TranslatedTitleInterfaz Cerebro Computador para inclusion laboral en individuos que sufren discapacidad de miembro superior
dc.typebachelorThesis
dc.type.documentTrabajo de grado
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersion
dc.type.spaTrabajo de grado
local.department.reportEscuela de Ciencias e Ingeniería
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