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

Los 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 electromiogr...

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Tipo de recurso:
Fecha de publicación:
2023
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/42320
Acceso en línea:
https://repository.urosario.edu.co/handle/10336/42320
Palabra clave:
Interfaz cerebro-ordenador
Potencial evocado visual en estado estacionario
Inclusión social
Juego serio
Discapacidad de miembro superior
Brain-Computer Interface
Steady State Visual Evoked Po- tential
Social inclusion
Serious game
Upper-limb disability
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License
Attribution-NonCommercial-NoDerivatives 4.0 International
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network_acronym_str EDOCUR2
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dc.title.none.fl_str_mv A Brain-Computer Interface for labour market inclusion of people suffering severe upper-limb impairments
dc.title.TranslatedTitle.none.fl_str_mv Interfaz Cerebro Computador para inclusion laboral en individuos que sufren discapacidad de miembro superior
title A Brain-Computer Interface for labour market inclusion of people suffering severe upper-limb impairments
spellingShingle A Brain-Computer Interface for labour market inclusion of people suffering severe upper-limb impairments
Interfaz cerebro-ordenador
Potencial evocado visual en estado estacionario
Inclusión social
Juego serio
Discapacidad de miembro superior
Brain-Computer Interface
Steady State Visual Evoked Po- tential
Social inclusion
Serious game
Upper-limb disability
title_short A Brain-Computer Interface for labour market inclusion of people suffering severe upper-limb impairments
title_full A Brain-Computer Interface for labour market inclusion of people suffering severe upper-limb impairments
title_fullStr A Brain-Computer Interface for labour market inclusion of people suffering severe upper-limb impairments
title_full_unstemmed A Brain-Computer Interface for labour market inclusion of people suffering severe upper-limb impairments
title_sort A Brain-Computer Interface for labour market inclusion of people suffering severe upper-limb impairments
dc.contributor.advisor.none.fl_str_mv Delisle Rodríguez, Denis
Jiménez Hernández, Mario Fernando
dc.subject.none.fl_str_mv Interfaz cerebro-ordenador
Potencial evocado visual en estado estacionario
Inclusión social
Juego serio
Discapacidad de miembro superior
topic Interfaz cerebro-ordenador
Potencial evocado visual en estado estacionario
Inclusión social
Juego serio
Discapacidad de miembro superior
Brain-Computer Interface
Steady State Visual Evoked Po- tential
Social inclusion
Serious game
Upper-limb disability
dc.subject.keyword.none.fl_str_mv Brain-Computer Interface
Steady State Visual Evoked Po- tential
Social inclusion
Serious game
Upper-limb disability
description Los 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.
publishDate 2023
dc.date.created.none.fl_str_mv 2023-06-10
dc.date.accessioned.none.fl_str_mv 2024-03-05T19:25:36Z
dc.date.available.none.fl_str_mv 2024-03-05T19:25:36Z
dc.type.none.fl_str_mv bachelorThesis
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.document.none.fl_str_mv Trabajo de grado
dc.type.spa.none.fl_str_mv Trabajo de grado
dc.identifier.uri.none.fl_str_mv https://repository.urosario.edu.co/handle/10336/42320
url https://repository.urosario.edu.co/handle/10336/42320
dc.language.iso.none.fl_str_mv eng
language eng
dc.rights.*.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
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dc.rights.acceso.none.fl_str_mv Abierto (Texto Completo)
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
Abierto (Texto Completo)
http://creativecommons.org/licenses/by-nc-nd/4.0/
http://purl.org/coar/access_right/c_abf2
dc.format.extent.none.fl_str_mv 82 pp
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad del Rosario
dc.publisher.department.none.fl_str_mv Escuela de Ingeniería, Ciencia y Tecnología
dc.publisher.program.none.fl_str_mv Programa de Matemáticas Aplicadas y Ciencias de la Computación - MACC
publisher.none.fl_str_mv Universidad del Rosario
institution Universidad del Rosario
dc.source.bibliographicCitation.none.fl_str_mv European Commission. Employment, Social Affairs & Inclusion. Website. 2023. url: https://ec.europa.eu/social/main.jsp?catId=1134&langId=en.
AK Dasgupta et al. “The performance of the ICEROSS prostheses amongst transtibial amputees with a special reference to the workplaceâa preliminary study”. In: Occupational medicine 47.4 (1997), pp. 228–236.
Robert Bogue. “Exoskeletons and robotic prosthetics: a review of recent developments”. In: Industrial Robot 36.5 (2009), pp. 421–427. doi: 10.1108/ 01439910910980141.
Kazuo Kiguchi, Takakazu Tanaka, and Toshio Fukuda. “Neuro-fuzzy control of a robotic exoskeleton with EMG signals”. In: IEEE Transactions on fuzzy systems 12.4 (2004), pp. 481–490.
Kazuo Kiguchi et al. “Development of a 3DOF mobile exoskeleton robot for human upper-limb motion assist”. In: Robotics and Autonomous systems 56.8 (2008), pp. 678–691.
Mark F Bear, Barry W Connors, and Michael A Paradiso. Neuroscience: Exploring the Brain. Lippincott Williams & Wilkins, 2007.
Silvija Angelova et al. “Power frequency spectrum analysis of surface EMG signals of upper limb muscles during elbow flexion–A comparison between healthy subjects and stroke survivors”. In: Journal of Electromyography and Kinesiology 38 (2018), pp. 7–16.
Institute of Entrepreneurship Development. Gender Equality in Physically Demanding Occupations. Project update on IED. May 2021. url: https://ied. eu / project - updates / gender - equality - in - physically - demanding - occupations/.
Jonathan R Wolpaw et al. “Brain-computer interface technology: a review of the first international meeting”. In: IEEE transactions on rehabilitation engineering 8.2 (2000), pp. 164–173.
Luis Fernando Nicolas-Alonso and Jaime Gomez-Gil. “Brain computer interfaces, a review”. In: sensors 12.2 (2012), pp. 1211–1279.
Swati Vaid, Preeti Singh, and Chamandeep Kaur. “EEG signal analysis for BCI interface: A review”. In: 2015 fifth international conference on advanced computing & communication technologies. IEEE. 2015, pp. 143–147.
Walter S Pritchard. “Psychophysiology of P300.” In: Psychological bulletin 89.3 (1981), p. 506.
Erwei Yin et al. “A novel hybrid BCI speller based on the incorporation of SSVEP into the P300 paradigm”. In: Journal of neural engineering 10.2 (2013), p. 026012.
Martin Lotze and Ulrike Halsband. “Motor imagery”. In: Journal of Physiologyparis 99.4-6 (2006), pp. 386–395.
Jennifer A Stevens and Mary Ellen Phillips Stoykov. “Using motor imagery in the rehabilitation of hemiparesis”. In: Archives of physical medicine and rehabilitation 84.7 (2003), pp. 1090–1092.
Aymeric Guillot and Christian Collet. “Construction of the motor imagery integrative model in sport: a review and theoretical investigation of motor imagery use”. In: International Review of Sport and Exercise Psychology 1.1 (2008), pp. 31–44.
Christoph Guger et al. “How many people could use an SSVEP BCI?” In: Frontiers in neuroscience 6 (2012), p. 169.
Danhua Zhu et al. “A survey of stimulation methods used in SSVEP-based BCIs”. In: Computational intelligence and neuroscience 2010 (2010), pp. 1–12.
Marcin Kołodziej et al. “Comparison of EEG signal preprocessing methods for SSVEP recognition”. In: 2016 39th International Conference on Telecommunications and Signal Processing (TSP). IEEE. 2016, pp. 340–345.
Richard MG Tello et al. “A comparison of techniques and technologies for SSVEP classification”. In: 5th ISSNIP-IEEE Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC). IEEE. 2014, pp. 1–6.
Eric Kandel et al. Principles of Neural Science. McGraw-Hill Education, 2013.
Steven M Chrysafides, Stephen J Bordes, and Sandeep Sharma. “Physiology, Resting Potential”. In: StatPearls (2022).
Max O Krucoff et al. “Enhancing nervous system recovery through neurobiologics, neural interface training, and neurorehabilitation”. In: Frontiers in neuroscience 10 (2016), p. 584.
Priyanka A. Abhang, Bharti W. Gawali, and Suresh C. Mehrotra. Introduction to EEG- and Speech-Based Emotion Recognition. Academic Press, 2016.
Rajesh P. N. Rao. Brain-Computer Interfacing: An Introduction. Cambridge University Press, 2013.
Hamilton Rivera-Flor et al. “CCA-Based Compressive Sensing for SSVEPBased Brain-Computer Interfaces to Command a Robotic Wheelchair”. In: IEEE Transactions on Instrumentation and Measurement 71 (2022), pp. 1– 10.
Brain Surgery: Treatment & Recovery. https://my.clevelandclinic.org/ health/treatments/16802-brain-surgery.
Hermann Hinrichs et al. “Comparison between a wireless dry electrode EEG system with a conventional wired wet electrode EEG system for clinical applications”. In: Scientific reports 10.1 (2020), pp. 1–14.
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spelling Delisle Rodríguez, Denis1d7e9821-48b6-4b5e-84f2-568502cf2b0e-1Jiménez Hernández, Mario Fernando5820750600García Osorio, Juan LucasProfesional en Matemáticas Aplicadas y Ciencias de la ComputaciónProfesional en Matemáticas Aplicadas y Ciencias de la ComputaciónPregradoFull timef1dec54c-c419-4114-ba7d-285036561494-12024-03-05T19:25:36Z2024-03-05T19:25:36Z2023-06-10Los 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.Robotic 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.82 ppapplication/pdfhttps://repository.urosario.edu.co/handle/10336/42320engUniversidad del RosarioEscuela de Ingeniería, Ciencia y TecnologíaPrograma de Matemáticas Aplicadas y Ciencias de la Computación - MACCAttribution-NonCommercial-NoDerivatives 4.0 InternationalAbierto (Texto Completo)http://creativecommons.org/licenses/by-nc-nd/4.0/http://purl.org/coar/access_right/c_abf2European Commission. Employment, Social Affairs & Inclusion. Website. 2023. url: https://ec.europa.eu/social/main.jsp?catId=1134&langId=en.AK Dasgupta et al. “The performance of the ICEROSS prostheses amongst transtibial amputees with a special reference to the workplaceâa preliminary study”. In: Occupational medicine 47.4 (1997), pp. 228–236.Robert Bogue. “Exoskeletons and robotic prosthetics: a review of recent developments”. In: Industrial Robot 36.5 (2009), pp. 421–427. doi: 10.1108/ 01439910910980141.Kazuo Kiguchi, Takakazu Tanaka, and Toshio Fukuda. “Neuro-fuzzy control of a robotic exoskeleton with EMG signals”. In: IEEE Transactions on fuzzy systems 12.4 (2004), pp. 481–490.Kazuo Kiguchi et al. “Development of a 3DOF mobile exoskeleton robot for human upper-limb motion assist”. In: Robotics and Autonomous systems 56.8 (2008), pp. 678–691.Mark F Bear, Barry W Connors, and Michael A Paradiso. Neuroscience: Exploring the Brain. Lippincott Williams & Wilkins, 2007.Silvija Angelova et al. “Power frequency spectrum analysis of surface EMG signals of upper limb muscles during elbow flexion–A comparison between healthy subjects and stroke survivors”. In: Journal of Electromyography and Kinesiology 38 (2018), pp. 7–16.Institute of Entrepreneurship Development. Gender Equality in Physically Demanding Occupations. Project update on IED. May 2021. url: https://ied. eu / project - updates / gender - equality - in - physically - demanding - occupations/.Jonathan R Wolpaw et al. “Brain-computer interface technology: a review of the first international meeting”. In: IEEE transactions on rehabilitation engineering 8.2 (2000), pp. 164–173.Luis Fernando Nicolas-Alonso and Jaime Gomez-Gil. “Brain computer interfaces, a review”. In: sensors 12.2 (2012), pp. 1211–1279.Swati Vaid, Preeti Singh, and Chamandeep Kaur. “EEG signal analysis for BCI interface: A review”. In: 2015 fifth international conference on advanced computing & communication technologies. IEEE. 2015, pp. 143–147.Walter S Pritchard. “Psychophysiology of P300.” In: Psychological bulletin 89.3 (1981), p. 506.Erwei Yin et al. “A novel hybrid BCI speller based on the incorporation of SSVEP into the P300 paradigm”. In: Journal of neural engineering 10.2 (2013), p. 026012.Martin Lotze and Ulrike Halsband. “Motor imagery”. In: Journal of Physiologyparis 99.4-6 (2006), pp. 386–395.Jennifer A Stevens and Mary Ellen Phillips Stoykov. “Using motor imagery in the rehabilitation of hemiparesis”. In: Archives of physical medicine and rehabilitation 84.7 (2003), pp. 1090–1092.Aymeric Guillot and Christian Collet. “Construction of the motor imagery integrative model in sport: a review and theoretical investigation of motor imagery use”. In: International Review of Sport and Exercise Psychology 1.1 (2008), pp. 31–44.Christoph Guger et al. “How many people could use an SSVEP BCI?” In: Frontiers in neuroscience 6 (2012), p. 169.Danhua Zhu et al. “A survey of stimulation methods used in SSVEP-based BCIs”. In: Computational intelligence and neuroscience 2010 (2010), pp. 1–12.Marcin Kołodziej et al. “Comparison of EEG signal preprocessing methods for SSVEP recognition”. In: 2016 39th International Conference on Telecommunications and Signal Processing (TSP). IEEE. 2016, pp. 340–345.Richard MG Tello et al. “A comparison of techniques and technologies for SSVEP classification”. In: 5th ISSNIP-IEEE Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC). IEEE. 2014, pp. 1–6.Eric Kandel et al. Principles of Neural Science. McGraw-Hill Education, 2013.Steven M Chrysafides, Stephen J Bordes, and Sandeep Sharma. “Physiology, Resting Potential”. In: StatPearls (2022).Max O Krucoff et al. “Enhancing nervous system recovery through neurobiologics, neural interface training, and neurorehabilitation”. In: Frontiers in neuroscience 10 (2016), p. 584.Priyanka A. Abhang, Bharti W. Gawali, and Suresh C. Mehrotra. Introduction to EEG- and Speech-Based Emotion Recognition. Academic Press, 2016.Rajesh P. N. Rao. Brain-Computer Interfacing: An Introduction. Cambridge University Press, 2013.Hamilton Rivera-Flor et al. “CCA-Based Compressive Sensing for SSVEPBased Brain-Computer Interfaces to Command a Robotic Wheelchair”. In: IEEE Transactions on Instrumentation and Measurement 71 (2022), pp. 1– 10.Brain Surgery: Treatment & Recovery. https://my.clevelandclinic.org/ health/treatments/16802-brain-surgery.Hermann Hinrichs et al. “Comparison between a wireless dry electrode EEG system with a conventional wired wet electrode EEG system for clinical applications”. 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