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Vol. 12 No. 1 (2022)
Enero-Junio

Articles

Engagement level analysis in the programming of haptic devices through a brain computer interface

https://doi.org/10.19053/20278306.v12.n1.2022.14214

Published 2022-02-15

  • César Augusto Peña-Cortés
    • ,
  • Andrés Leonardp Vargas-Granados
    • ,
  • Aldo Pardo-García

César Augusto Peña-Cortés
Universidad de Pamplona, Pamplona

Andrés Leonardp Vargas-Granados
Universidad de Pamplona, Pamplona

Aldo Pardo-García
Universidad de Pamplona, Pamplona

Abstract

This paper proposes the use of a brain-computer interface, which allows measuring the neurosignals of the level of commitment in the performance of the telemarketer when performing the task. The methodology to determine the level of haptic assists consists of 4 steps: first, a teleoperation application is selected, which consisted of remotely moving a robot along a predefined path. In steps two and three, the proposed task is executed with and without haptic assistance. In step 4, the robot's paths are analyzed to determine the areas where the operator required a higher or lower level of assistance. The results show that the user takes a significantly longer time to complete the proposed paths, when the haptic assists are not active. It is concluded that the brain-computer interfaces allow detecting the areas where these aids are more necessary and the areas where they can be reduced.

Keywords

brain-computer interface, haptics, neuronsignals, engagement

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Author Biography

César Augusto Peña-Cortés

Ingeniero Electromecánico, Doctor en Automática y Robótica,

Andrés Leonardp Vargas-Granados

Ingeniero Mecatrónico, Magíster en Controles Industriales

Aldo Pardo-García

Ingeniero Eléctrico,  Doctor en Complejos Eléctricos y Electrotécnicos,

References

  1. Acı, Ç. İ., Kaya, M., & Mishchenko, Y. (2019). Distinguishing mental attention states of humans via an EEG-based passive BCI using machine learning methods. Expert Systems with Applications, 134, 153–166. https://doi.org/10.1016/j.eswa.2019.05.057 DOI: https://doi.org/10.1016/j.eswa.2019.05.057
  2. Ackerman, E. (2019). The underwater transformer: Ex-NASA engineers built a robot sub that transforms into a skilled humanoid. IEEE Spectrum, 56 (8), 22–29. https://doi.org/10.1109/MSPEC.2019.8784119 DOI: https://doi.org/10.1109/MSPEC.2019.8784119
  3. Adamos, D. A., Dimitriadis, S. I., & Laskaris, N. A. (2016). Towards the bio-personalization of music recommendation systems: A single-sensor EEG biomarker of subjective music preference. Information Sciences, 343–344, 94–108. https://doi.org/10.1016/j.ins.2016.01.005 DOI: https://doi.org/10.1016/j.ins.2016.01.005
  4. Aggravi, M., Pacchierotti, C., & Giordano, P. R. (2021). Connectivity-Maintenance Teleoperation of a UAV Fleet With Wearable Haptic Feedback. IEEE Transactions on Automation Science and Engineering, 18 (3), 1243–1262. https://doi.org/10.1109/TASE.2020.3000060 DOI: https://doi.org/10.1109/TASE.2020.3000060
  5. Alvernia-Acevedo, S. A., & Rico-Bautista, D. (2017). Análisis de una red en un entorno IPV6: una mirada desde las intrusiones de red y el modelo TCP/IP. Revista Colombiana de Tecnologías de Avanzada, 1 (29), 81–91. DOI: https://doi.org/10.24054/16927257.v29.n29.2017.2490
  6. Arvaneh, M., Umilta, A., & Robertson, I. H. (2015). Filter bank common spatial patterns in mental workload estimation. En 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 4749–4752. Milan, Italy. https://doi.org/10.1109/EMBC.2015.7319455 DOI: https://doi.org/10.1109/EMBC.2015.7319455
  7. Cantillo-Maldonado, A., Gualdrón-Guerrero, O., & Ortíz-Sandoval, J. (2018). Procesamiento de señales EMG en un sistema embebido para el control neuronal de un brazo robótico. Revista Colombiana de Tecnologías de Avanzada, 2 (32), 139–147. https://doi.org/10.24054/16927257.v32.n32.2018.3037 DOI: https://doi.org/10.24054/16927257.v32.n32.2018.3037
  8. Castro-Casadiego, S. A., Niño-Rondón, C. V., & Medina-Delgado, B. (2020). Caracterización para la ubicación en la captura de video aplicado a técnicas de visión artificial en la detección de personas. Revista Colombiana de Tecnologías de Avanzada, 2 (36), 83–88. DOI: https://doi.org/10.24054/16927257.v36.n36.2020.24
  9. Chen, T., Saadatnia, Z., Kim, J., Looi, T., Drake, J., Diller, E., & Naguib, H. E. (2021). Novel, Flexible, and Ultrathin Pressure Feedback Sensor for Miniaturized Intraventricular Neurosurgery Robotic Tools. IEEE Transactions on Industrial Electronics, 68 (5), 4415–4425. https://doi.org/10.1109/TIE.2020.2984427 DOI: https://doi.org/10.1109/TIE.2020.2984427
  10. Córdova, F., Díaz, H., Cifuentes, F., Cañete, L., & Palominos, F. (2015). Identifying problem solving strategies for learning styles in engineering students subjected to intelligence test and EEG monitoring. Procedia - Procedia Computer Science, 55 (Itqm), 18–27. https://doi.org/10.1016/j.procs.2015.07.003 DOI: https://doi.org/10.1016/j.procs.2015.07.003
  11. Dijk, W. A., Velde, W., M., Van der W. J., Kolkman, H. J. G., & M. Crijns, K. I. L. (1995). Integration of the Marquette ECG management system into the Department Information System using the European SCP-ECG Standard. In A. Murray (Ed.), COMPUTERS IN CARDIOLOGY 1995, 397-400. IEEE (The Institute of Electrical and Electronics Engineers). DOI: https://doi.org/10.1109/CIC.1995.482669
  12. Du, G., Han, R., Yao, G., Ng, W., & Li, D. (2021). A Gesture and Speech-guided Robot Teleoperation Method Based on Mobile Interaction with Unrestricted Force Feedback. IEEE/ASME Transactions on Mechatronics, 4435(c). https://doi.org/10.1109/TMECH.2021.3064581 DOI: https://doi.org/10.1109/TMECH.2021.3064581
  13. Eid, M. (2013). Read Go Go!: Towards real-time notification on readers’ state of attention. En XXIV International Symposium on Information, Communication and Automation Technologies (ICAT) 1–6. http://ieeexplore.ieee.org/document/6684047/ DOI: https://doi.org/10.1109/ICAT.2013.6684047
  14. Emotiv (2017). Open Your Mind to Next Generation Brainwe EMOTIV Insight Introduction Vid. https://www.emotiv.com
  15. Fernández-Samacá, L., Mesa-Mesa, A. L., & Pérez-Holguín, W. J. (2016). Formative Research for Engineering Students By Using Robotics. Revista Colombiana de Tecnologias de Avanzada, 2 (28), 30–38. https://doi.org/https://doi.org/10.24054/16927257.v28.n28.2016.2461 DOI: https://doi.org/10.24054/16927257.v28.n28.2016.2461
  16. Ferre, M., Buss, M., Aracil, R., Melchiorri, C., & Balaguer, C. (2007). Advances in Telerobotics 31. Berlin, Heidelberg: Springer Berlin Heidelberg. DOI: https://doi.org/10.1007/978-3-540-71364-7
  17. Gómez-Monsalve, P. A., & Durán-Acevedo, C. M. (2017). Nariz Electrónica Inalámbrica para Control de Emisiones de Gases en Minas de Carbón. Revista Colombiana de Tecnologías de Avanzada, 1 (29), 118–125. DOI: https://doi.org/10.24054/16927257.v29.n29.2017.2526
  18. Gutiérrez, J., Calderón, I., Servín, R., Moreno, H., Barrera, M., & Adán, R. (2017). De una Mano Mecánica Impresa en 3D a una Prótesis Mioeléctrica a Bajo Costo (Parte I: interfaz EMG). Revista Colombiana de Tecnologías de Avanzada, 2 (30), 63–71. DOI: https://doi.org/10.24054/16927257.v30.n30.2017.2746
  19. Hashtrudi-Zaad, K., & Salcudean, S. E. (2001). Analysis of Control Architectures for Teleoperation Systems with Impedance/Admittance Master and Slave Manipulators. The International Journal of Robotics Research, 20 (6), 419–445. https://doi.org/10.1177/02783640122067471 DOI: https://doi.org/10.1177/02783640122067471
  20. Hong, A., Petruska, A. J., Zemmar, A., & Nelson, B. J. (2021). Magnetic Control of a Flexible Needle in Neurosurgery. IEEE Transactions on Biomedical Engineering, 68 (2), 616–627. https://doi.org/10.1109/TBME.2020.3009693 DOI: https://doi.org/10.1109/TBME.2020.3009693
  21. Ijjada, M. S., Thapliyal, H., Caban-Holt, A., & Arabnia, H. R. (2015). Evaluation of wearable head set devices in older adult populations for research. En IEEE (Ed.), Proceedings - 2015 International Conference on Computational Science and Computational Intelligence, CSCI 2015, 810–811. Las Vegas, USA. https://doi.org/10.1109/CSCI.2015.158 DOI: https://doi.org/10.1109/CSCI.2015.158
  22. Jiménez-Moreno, R., Espinosa-Valcárcel, F., & Amaya-Hurtado, D. (2013). Teleoperated systems: a perspective on telesurgery applications. Revista Ingeniería Biomédica, 7 (14), 30-41.
  23. Khalili-Ardali, M., Rana, A., Pourmohammad, M., Birbaumer, N., & Chaudhary, U. (2019). Semantic and BCI-performance in completely paralyzed patients: Possibility of language attrition in completely locked in syndrome. Brain and Language, 194, 93–97. https://doi.org/10.1016/j.bandl.2019.05.004 DOI: https://doi.org/10.1016/j.bandl.2019.05.004
  24. Katona, J., Farkas, I., Ujbanyi, P., Dukan, A., & Kovari, A. (2014). Evaluation of the NeuroSky MindFlex EEG headset brain waves data. IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI) 91–94. DOI: https://doi.org/10.1109/SAMI.2014.6822382
  25. Lambraño-García, E. D., Lázaro-Plata, J. L., & Trigos-Quintero, A. E. (2017). Revisión de técnicas de sistemas de visión artificial para la inspección de procesos de soldadura tipo GMAW. Revista Colombiana de Tecnologías de Avanzada, 1 (29), 47–57. https://doi.org/https://doi.org/10.24054/16927257.v29.n29.2017.2486 DOI: https://doi.org/10.24054/16927257.v29.n29.2017.2486
  26. Lee, J., Zhang, X., Park, C. H., & Kim, M. J. (2021). Real-Time Teleoperation of Magnetic Force-Driven Microrobots With 3D Haptic Force Feedback for Micro-Navigation and Micro-Transportation. IEEE Robotics and Automation Letters, 6 (2), 1769–1776. https://doi.org/10.1109/LRA.2021.3060708 DOI: https://doi.org/10.1109/LRA.2021.3060708
  27. León-Rodríguez, H., & Murcia-Rivera, D. (2018). Micro Robots Controlados por Actuadores Electromagnéticos en Aplicaciones Médicas. Revista Colombiana de Tecnologías de Avanzada, 2 (32), 31–36. DOI: https://doi.org/10.24054/16927257.v32.n32.2018.3024
  28. Li, W., Guo, J., Ding, L., Wang, J., & Gao, H. (2021). Slippage-Dependent Teleoperation of Wheeled Mobile Robots on Soft Terrains. IEEE Robotics and Automation Letters, 6 (3), 4962–4969. https://doi.org/10.1109/LRA.2021.3070295 DOI: https://doi.org/10.1109/LRA.2021.3070295
  29. Martin, S. (2009). Characterisation of the Novint Falcon Haptic Device for Application as a Robot Manipulator. Sydney, Australia.
  30. Moreno, L., Peña, C., & Gualdron, O. (2014). Desarrollo de un sistema de neuromarketing usando el dispositivo Emotiv-Epoc. Redes de Ingeniería, 5 (2), 6–15. DOI: https://doi.org/10.14483/2248762X.8042
  31. Moreno-Cueva, L. Á., Peña-Cortés, C. A., & González-Sepúlveda, H. (2014). Integration of a Neurosignals System to Detect Human Expressions in the Multimedia Material Analysis. Revista Facultad de Ingeniería, 24 (38), 29–40. https://doi.org/10.19053/01211129.3156 DOI: https://doi.org/10.19053/01211129.3156
  32. Nakisa, B., Rastgoo, M. N., Tjondronegoro, D., & Chandran, V. (2018). Evolutionary Computation Algorithms for Feature Selection of EEG-based Emotion Recognition using Mobile Sensors. Expert Systems with Applications, 93, 143-155. https://doi.org/10.1016/j.eswa.2017.09.062 DOI: https://doi.org/10.1016/j.eswa.2017.09.062
  33. Peña-Cortés, C. A., Gualdron, O. E., & Moreno-Contreras, G. G. (2014). Warning and Rehabilitation System Using Brain Computer Interface (BCI) in Cases of Bruxism. Ingenieria y Universidad, 18 (1), 177–193. https://doi.org/10.11144/Javeriana.IYU18-1.sarb DOI: https://doi.org/10.11144/Javeriana.IYU18-1.sarb
  34. Peña, C., Caicedo, S., Moreno, L., Maestre, M., & Pardo, A. (2017). Use of a Low Cost Neurosignals Capture System to Show the Importance of Developing Didactic Activities Within a Class to Increase the Level of Student Engagement. (Case Study). WSEAS Transaction on Computers, 16, 172–178.
  35. Rico-Castrillo, E. D., García-Pabón, J. J., & Bermúdez-Santaella, J. R. (2020). Implementation of the electrical-electronic system and software system of a CNC machine. Revista Colombiana de Tecnologías de Avanzada, 2 (36). DOI: https://doi.org/10.24054/16927257.v36.n36.2020.16
  36. Rivera-Guerrero, M. A., Guadrón-Guerrero, O. E., & Torres-Chávez, I. (2020). Detección de pesticidas en el durazno (prunus pérsica) mediante una nariz electrónica. Revista de Investigación, Desarrollo e Innovación, 10 (2), 359-365. https://doi.org/10.19053/20278306.v10.n2.2020.10724 DOI: https://doi.org/10.19053/20278306.v10.n2.2020.10724
  37. Rossi, L. S. (2020). Notas sobre la comunicación táctil y el estudio de los medios hápticos. La Trama de la Comunicación, 24 (2), 33-51. DOI: https://doi.org/10.35305/lt.v24i2.743
  38. Soler, M., Rodríguez, H., & Peña, C. (2014). Desarrollo de un robot explorador operado mediante neuroseñales. Revista Politécnica, 10(19), 125–134.
  39. Trejos-Salazar, D. F., Duque-Hurtado, P. L., Montoya-Restrepo, L. A., & Montoya-Restrepo, I. A. (2021). Neuroeconomía: Una revisión basada en técnicas de mapeo científico. Revista de Investigación, Desarrollo e Innovación, 11 (2), 243-260. https://doi.org/10.19053/20278306.v11.n2.2021.12754 DOI: https://doi.org/10.19053/20278306.v11.n2.2021.12754
  40. Zeng, W., Yan, J., Yan, K., Huang, X., Wang, X., & Cheng, S. S. (2021). Modeling a Symmetrically-notched Continuum Neurosurgical Robot with Non-constant Curvature and Superelastic Property. IEEE Robotics and Automation Letters, 6 (4). https://doi.org/10.1109/LRA.2021.3094475 DOI: https://doi.org/10.1109/LRA.2021.3094475

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