Evaluación de las emociones de usuarios en tareas con realimentación háptica utilizado el dispositivo Emotiv Insight

Introduction− This study assesses the five-performance metrics, available on the Emotive Insight device in a virtual toolpath tracking task through a mobile robot. Objective− Characterize and/or determine if some EEG metrics are related to primitives of a teleoperation task, where haptic feedback is...

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Autores:
Corredor Camargo, Javier Adolfo
Peña Cortes, Cesar Augusto
Pardo Garcia, Aldo
Tipo de recurso:
Article of journal
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
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spa
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/5595
Acceso en línea:
https://hdl.handle.net/11323/5595
https://doi.org/10.17981/ingecuc.15.1.2019.01
https://repositorio.cuc.edu.co/
Palabra clave:
Telerobótica
Robots móviles
Control compartido
Háptica
EEG
Interfaz cerebro computador
Telerobotics
Brain computer
Interface
Mobile robots
Shared control
Haptics
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_4c4d0b0cb9df9471fb694200ed7df929
oai_identifier_str oai:repositorio.cuc.edu.co:11323/5595
network_acronym_str RCUC2
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dc.title.spa.fl_str_mv Evaluación de las emociones de usuarios en tareas con realimentación háptica utilizado el dispositivo Emotiv Insight
dc.title.translated.spa.fl_str_mv Assessment of the users emotions in haptic feedback tasks using the Emotiv Insight device
title Evaluación de las emociones de usuarios en tareas con realimentación háptica utilizado el dispositivo Emotiv Insight
spellingShingle Evaluación de las emociones de usuarios en tareas con realimentación háptica utilizado el dispositivo Emotiv Insight
Telerobótica
Robots móviles
Control compartido
Háptica
EEG
Interfaz cerebro computador
Telerobotics
Brain computer
Interface
Mobile robots
Shared control
Haptics
title_short Evaluación de las emociones de usuarios en tareas con realimentación háptica utilizado el dispositivo Emotiv Insight
title_full Evaluación de las emociones de usuarios en tareas con realimentación háptica utilizado el dispositivo Emotiv Insight
title_fullStr Evaluación de las emociones de usuarios en tareas con realimentación háptica utilizado el dispositivo Emotiv Insight
title_full_unstemmed Evaluación de las emociones de usuarios en tareas con realimentación háptica utilizado el dispositivo Emotiv Insight
title_sort Evaluación de las emociones de usuarios en tareas con realimentación háptica utilizado el dispositivo Emotiv Insight
dc.creator.fl_str_mv Corredor Camargo, Javier Adolfo
Peña Cortes, Cesar Augusto
Pardo Garcia, Aldo
dc.contributor.author.spa.fl_str_mv Corredor Camargo, Javier Adolfo
Peña Cortes, Cesar Augusto
Pardo Garcia, Aldo
dc.subject.proposal.spa.fl_str_mv Telerobótica
Robots móviles
Control compartido
Háptica
EEG
topic Telerobótica
Robots móviles
Control compartido
Háptica
EEG
Interfaz cerebro computador
Telerobotics
Brain computer
Interface
Mobile robots
Shared control
Haptics
dc.subject.proposal.eng.fl_str_mv Interfaz cerebro computador
Telerobotics
Brain computer
Interface
Mobile robots
Shared control
Haptics
description Introduction− This study assesses the five-performance metrics, available on the Emotive Insight device in a virtual toolpath tracking task through a mobile robot. Objective− Characterize and/or determine if some EEG metrics are related to primitives of a teleoperation task, where haptic feedback is provided, in order to verify if it can be useful to incorporate the information available from the Emotiv device in a shared control strategy. Methodology− An experimental design was formulated, which includes the recording and analysis of neurosigns in five users with a Brain Computer Interface (BCI), executing tasks of teleoperation of a mobile robot in the Environment of VREP (Virtual Robot Experimentation Platform). Results− The results show that engagement and relaxation are emotions that could be useful to identify demanding situations in tracking path and evasion of obstacles such as the experimental setup proposed in this article. On the other hand, it is observed that some metrics such as stress, excitement, interest and focus, on average, remain at similar levels during the task execution. Conclusions− Including brain computer interfaces of low-cost, such as the Emotiv in tasks with haptic feedback, offers new possibilities for assessment user performance and potential for control applications.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-11-12T15:20:50Z
dc.date.available.none.fl_str_mv 2019-11-12T15:20:50Z
dc.date.issued.none.fl_str_mv 2019-03-01
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.citation.spa.fl_str_mv J. Corredor, C. Peña, A. Pardo, “Evaluación de las emociones de usuarios en tareas con realimentación háptica utilizado el dispositivo Emotiv Insight,” INGE CUC, vol. 15, no. 1, pp. 9-16, 2019. DOI: http://doi.org/10.17981/ingecuc.15.1.2019.01
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identifier_str_mv J. Corredor, C. Peña, A. Pardo, “Evaluación de las emociones de usuarios en tareas con realimentación háptica utilizado el dispositivo Emotiv Insight,” INGE CUC, vol. 15, no. 1, pp. 9-16, 2019. DOI: http://doi.org/10.17981/ingecuc.15.1.2019.01
10.17981/ingecuc.15.1.2019.01
2382-4700
Corporación Universidad de la Costa
0122-6517
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/5595
https://doi.org/10.17981/ingecuc.15.1.2019.01
https://repositorio.cuc.edu.co/
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dc.relation.references.spa.fl_str_mv C. Passenberg, A. Glaser, and A. Peer, “Exploring the Design Space of Haptic Assistants: The Assistance Policy Module”, IEEE Transactions on Haptics, vol. 6, no. 4, pp. 440-452, Oct. 2013. [Online]. https://doi.org/10.1109/TOH.2013.34
K. Holewa and A. Nawrocka, “Emotiv EPOC neuroheadset in brain-computer interface”, Proceedings of the 2014 15th International Carpathian Control Conference (ICCC). pp. 149–152, May. 2014. https://doi.org/10.1109/CarpathianCC.2014.6843587
G. S. Taylor and C. Schmidt, “Empirical Evaluation of the Emotiv EPOC BCI Headset for the Detection of Mental Actions”, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 56, no. 1, pp. 193–197, Sep. 2012. https://doi.org/10.1177/1071181312561017
R. Lievesley, M. Wozencroft and D. Ewins, “The Emotiv EPOC neuroheadset: an inexpensive method of controlling assistive technologies using facial expressions and thoughts?”, Journal of Assistive Technologies, vol. 5, no. 2, pp. 67–82, Jun. 2011. https://doi.org/10.1108/17549451111149278
R. Maskeliunas, R. Damasevicius, Martisius and M. Vasiljevas,“Consumer-grade EEG devices: are they usable for control tasks?,PeerJ 4, e1746, Mar. 2016. https://doi.org/10.7717/peerj.1746
C.-L. Lin, F.-Z. Shaw, K.-Y. Young, C.-T. Lin and T.-P. Jung,“EEG correlates of haptic feedback in a visuomotor tracking task”, NeuroImage, vol. 60, no. 4, pp. 2258–2273, May. 2012. https://doi.org/10.1016/j.neuroimage.2012.02.008
M. Grunwald, T. Weiss, W. Krause, L. Beyer, R. Rost, I. Gutberlet and H.-J. Gertz, “Power of theta waves in the EEG of human subjects increases during recall of haptic information”, Neuroscience Letters, vol. 260, no. 3, pp. 189–192, Feb. 1999. https://doi.org/10.1016/S0304-3940(98)00990-2
H. Miura, J. Kimura, N. Matsuda, M. Soga and H, Taki, “Classification of Haptic Tasks based on Electroencephalogram Frequency Analysis”, Procedia Computer Science, vol. 35, Supplement (C), pp. 1270–1277, Jan. 2014. https://doi.org/10.1016/j.procs.2014.08.226
W. Jia, Y. Luo, Y. Hu and J. Zhang, “Adaptive Force Control Tasks Have Far-Transfer Effect on Sustained Attention”, 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), vol. 2, pp. 212–217. Aug. 2017. https://doi.org/10.1109/IHMSC.2017.162
T. Palomaki, “EEG-based brain-computer interface with visual and haptic feedback”, Master’s thesis, Helsinki University of Technology, 2007. Available: http://lib.tkk.fi/Dipl/2007/urn007655.pdf
A. Chatterjee, V. Aggarwal, A. Ramos, S. Acharya, N. V. Thakor, “Operation of a Brain-Computer Interface Using Vibrotactile Biofeedback”, 3rd International IEEE/EMBS Conference on Neural Engineering. pp. 171–174, May. 2007. https://doi.org/10.1109/CNE.2007.369639
L. George, M. Marchal, L. Glondu and A. Lecuyer, “Combining Brain-Computer Interfaces and Haptics: Detecting Mental Workload to Adapt Haptic Assistance”, Haptics: Perception, Devices, Mobility, and Communication. Springer, Berlin, Heidelberg, pp. 124–135, Jun. 2012. https://doi.org/10.1007/978-3-642-31401-8_12
M. A. Benloucif, C. Sentouh, J. Floris, P. Simon and J. C. Popieul, “Online adaptation of the Level of Haptic Authority in a lane keeping system considering the driver’s state”, Transportation Research Part F: Traffic Psychology and Behaviour, In press, Sep. 2017. https://doi.org/10.1016/j.trf.2017.08.013
E. Rohmer, S. P. N. Singh and M. Freese, “V-REP: A versatile and scalable robot simulation framework”, IEEE/
RSJ International Conference on Intelligent Robots and Systems, pp. 1321–1326, Nov. 2013. https://doi.org/10.1109/IROS.2013.6696520
F. Conti, F. Barbagli, R. Balaniuk, M. Halg, C. Lu, D. Morris, L. Sentis, J. Warren, O. Khatib and K. Salisbury, “The CHAI libraries”, Proceedings of Eurohaptics 2003, Dublin, Ireland, pp. 496–500, 2003.
D. J. Block, M. B. Michelotti and R. S. Sreenivas, “Application of the Novint Falcon haptic device as an actuator in real-time control”, Paladyn, Journal of Behavioral Robotics, vol. 4, no. 3, 182–193, 2013. https://doi.org/10.2478/pjbr-2013-0017
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spelling Corredor Camargo, Javier AdolfoPeña Cortes, Cesar AugustoPardo Garcia, Aldo2019-11-12T15:20:50Z2019-11-12T15:20:50Z2019-03-01J. Corredor, C. Peña, A. Pardo, “Evaluación de las emociones de usuarios en tareas con realimentación háptica utilizado el dispositivo Emotiv Insight,” INGE CUC, vol. 15, no. 1, pp. 9-16, 2019. DOI: http://doi.org/10.17981/ingecuc.15.1.2019.01https://hdl.handle.net/11323/5595https://doi.org/10.17981/ingecuc.15.1.2019.0110.17981/ingecuc.15.1.2019.012382-4700Corporación Universidad de la Costa0122-6517REDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Introduction− This study assesses the five-performance metrics, available on the Emotive Insight device in a virtual toolpath tracking task through a mobile robot. Objective− Characterize and/or determine if some EEG metrics are related to primitives of a teleoperation task, where haptic feedback is provided, in order to verify if it can be useful to incorporate the information available from the Emotiv device in a shared control strategy. Methodology− An experimental design was formulated, which includes the recording and analysis of neurosigns in five users with a Brain Computer Interface (BCI), executing tasks of teleoperation of a mobile robot in the Environment of VREP (Virtual Robot Experimentation Platform). Results− The results show that engagement and relaxation are emotions that could be useful to identify demanding situations in tracking path and evasion of obstacles such as the experimental setup proposed in this article. On the other hand, it is observed that some metrics such as stress, excitement, interest and focus, on average, remain at similar levels during the task execution. Conclusions− Including brain computer interfaces of low-cost, such as the Emotiv in tasks with haptic feedback, offers new possibilities for assessment user performance and potential for control applications.Introducción: Este estudio evalúa las cinco métricas de desempeño, disponibles en el dispositivo Emotiv Insight en una tarea virtual de seguimiento de trayectorias por medio de un robot móvil. Objetivo: Caracterizar y/o determinar si algunas métricas EEG se relacionan con primitivas de una tarea de tele operación, donde se realimentan señales hápticas, en pro de verificar si puede ser útil incorporar la información disponible del dispositivo Emotiv en una estrategia de control compartido. Metodología: Se formuló un diseño experimental, que incluye el registro y análisis de neuroseñales en cinco usuarios con una Interfaz Cerebro Computador (ICC), ejecutando tareas de teleoperación de un robot móvil en el entorno de VREP (Virtual Robot Experimentation Platform). Resultados: Los resultados muestran que el compromiso y la relajación son emociones que podrían ser de utilidad para identificar situaciones demandantes en tareas de seguimiento y evasión de obstáculos. Por otro lado, se observa que algunas métricas como estrés, excitación, interés y enfoque, en promedio, se mantienen en niveles similares durante la ejecución de la tarea. Conclusiones: Incluir interfaces cerebro computador de bajo costo, como el Emotiv en tareas con realimentación háptica, ofrece nuevas posibilidades para la evaluación del desempeño del usuario y potencialmente para control.Corredor Camargo, Javier Adolfo-0000-0002-0106-8790-600Peña Cortes, Cesar Augusto-0000-0003-4148-2168-600Pardo Garcia, Aldo-0000-0003-2040-9420-6008 páginasapplication/pdfspaCorporación Universidad de la CostaINGE CUC; Vol. 15, Núm. 1 (2019)INGE CUCINGE CUCC. Passenberg, A. Glaser, and A. Peer, “Exploring the Design Space of Haptic Assistants: The Assistance Policy Module”, IEEE Transactions on Haptics, vol. 6, no. 4, pp. 440-452, Oct. 2013. [Online]. https://doi.org/10.1109/TOH.2013.34K. Holewa and A. Nawrocka, “Emotiv EPOC neuroheadset in brain-computer interface”, Proceedings of the 2014 15th International Carpathian Control Conference (ICCC). pp. 149–152, May. 2014. https://doi.org/10.1109/CarpathianCC.2014.6843587G. S. Taylor and C. Schmidt, “Empirical Evaluation of the Emotiv EPOC BCI Headset for the Detection of Mental Actions”, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 56, no. 1, pp. 193–197, Sep. 2012. https://doi.org/10.1177/1071181312561017R. Lievesley, M. Wozencroft and D. Ewins, “The Emotiv EPOC neuroheadset: an inexpensive method of controlling assistive technologies using facial expressions and thoughts?”, Journal of Assistive Technologies, vol. 5, no. 2, pp. 67–82, Jun. 2011. https://doi.org/10.1108/17549451111149278R. Maskeliunas, R. Damasevicius, Martisius and M. Vasiljevas,“Consumer-grade EEG devices: are they usable for control tasks?,PeerJ 4, e1746, Mar. 2016. https://doi.org/10.7717/peerj.1746C.-L. Lin, F.-Z. Shaw, K.-Y. Young, C.-T. Lin and T.-P. Jung,“EEG correlates of haptic feedback in a visuomotor tracking task”, NeuroImage, vol. 60, no. 4, pp. 2258–2273, May. 2012. https://doi.org/10.1016/j.neuroimage.2012.02.008M. Grunwald, T. Weiss, W. Krause, L. Beyer, R. Rost, I. Gutberlet and H.-J. Gertz, “Power of theta waves in the EEG of human subjects increases during recall of haptic information”, Neuroscience Letters, vol. 260, no. 3, pp. 189–192, Feb. 1999. https://doi.org/10.1016/S0304-3940(98)00990-2H. Miura, J. Kimura, N. Matsuda, M. Soga and H, Taki, “Classification of Haptic Tasks based on Electroencephalogram Frequency Analysis”, Procedia Computer Science, vol. 35, Supplement (C), pp. 1270–1277, Jan. 2014. https://doi.org/10.1016/j.procs.2014.08.226W. Jia, Y. Luo, Y. Hu and J. Zhang, “Adaptive Force Control Tasks Have Far-Transfer Effect on Sustained Attention”, 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), vol. 2, pp. 212–217. Aug. 2017. https://doi.org/10.1109/IHMSC.2017.162T. Palomaki, “EEG-based brain-computer interface with visual and haptic feedback”, Master’s thesis, Helsinki University of Technology, 2007. Available: http://lib.tkk.fi/Dipl/2007/urn007655.pdfA. Chatterjee, V. Aggarwal, A. Ramos, S. Acharya, N. V. Thakor, “Operation of a Brain-Computer Interface Using Vibrotactile Biofeedback”, 3rd International IEEE/EMBS Conference on Neural Engineering. pp. 171–174, May. 2007. https://doi.org/10.1109/CNE.2007.369639L. George, M. Marchal, L. Glondu and A. Lecuyer, “Combining Brain-Computer Interfaces and Haptics: Detecting Mental Workload to Adapt Haptic Assistance”, Haptics: Perception, Devices, Mobility, and Communication. Springer, Berlin, Heidelberg, pp. 124–135, Jun. 2012. https://doi.org/10.1007/978-3-642-31401-8_12M. A. Benloucif, C. Sentouh, J. Floris, P. Simon and J. C. Popieul, “Online adaptation of the Level of Haptic Authority in a lane keeping system considering the driver’s state”, Transportation Research Part F: Traffic Psychology and Behaviour, In press, Sep. 2017. https://doi.org/10.1016/j.trf.2017.08.013E. Rohmer, S. P. N. Singh and M. Freese, “V-REP: A versatile and scalable robot simulation framework”, IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1321–1326, Nov. 2013. https://doi.org/10.1109/IROS.2013.6696520F. Conti, F. Barbagli, R. Balaniuk, M. Halg, C. Lu, D. Morris, L. Sentis, J. Warren, O. Khatib and K. Salisbury, “The CHAI libraries”, Proceedings of Eurohaptics 2003, Dublin, Ireland, pp. 496–500, 2003.D. J. Block, M. B. Michelotti and R. S. Sreenivas, “Application of the Novint Falcon haptic device as an actuator in real-time control”, Paladyn, Journal of Behavioral Robotics, vol. 4, no. 3, 182–193, 2013. https://doi.org/10.2478/pjbr-2013-0017169115INGE CUCCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2INGE CUChttps://revistascientificas.cuc.edu.co/ingecuc/article/view/2048Evaluación de las emociones de usuarios en tareas con realimentación háptica utilizado el dispositivo Emotiv InsightAssessment of the users emotions in haptic feedback tasks using the Emotiv Insight deviceArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionTelerobóticaRobots móvilesControl compartidoHápticaEEGInterfaz cerebro computadorTeleroboticsBrain computerInterfaceMobile robotsShared controlHapticsPublicationCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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