skip to main content
10.1145/3334480.3382981acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
abstract

Changing Minds: Exploring Brain-Computer Interface Experiences with High School Students

Published:25 April 2020Publication History

ABSTRACT

Relatively little research exists on the use of experiences with EEG devices to support brain-computer interface (BCI) education. In this paper, we draw on techniques from BCI, visual programming languages, and computer science education to design a web-based environment for BCI education. We conducted a study with 14 10th and 11th grade high school students to investigate the effects of EEG experiences on students' BCI self-efficacy. We also explored the usability of a hybrid block-flow based visual interface for students new to BCI. Our results suggest that experiences with EEG devices may increase high school students' BCI self-efficacy. Furthermore, our findings offer insights for engaging high school students in BCI.

References

  1. Marvin Andujar and Juan E Gilbert. 2013. Let's learn!: enhancing user's engagement levels through passive brain-computer interfaces. In CHI'13 Extended Abstracts on Human Factors in Computing Systems. ACM, 703--708.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Alissa N Antle, Leslie Chesick, Aaron Levisohn, Srilekha Kirshnamachari Sridharan, and Perry Tan. 2015. Using neurofeedback to teach self-regulation to children living in poverty. In Proceedings of the 14th International Conference on Interaction Design and Children. ACM, 119--128.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Alissa N Antle, Elgin-Skye McLaren, Holly Fiedler, and Naomi Johnson. 2019. Evaluating the impact of a mobile neurofeedback app for young children at school and home. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, 36.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Aaron Bangor, Philip T Kortum, and James T Miller. 2008. An empirical evaluation of the system usability scale. Intl. Journal of Human--Computer Interaction 24, 6 (2008), 574--594.Google ScholarGoogle ScholarCross RefCross Ref
  5. N. Birbaumer, N. Ghanayim, T. Hinterberger, I. Iversen, B. Kotchoubey, A. Kübler, J. Perelmouter, E. Taub, and H. Flor. 1999. A spelling device for the paralysed. Nature 398, 6725 (1999), 297--298. DOI: http://dx.doi.org/10.1038/18581Google ScholarGoogle ScholarCross RefCross Ref
  6. Karen Brennan and Mitchel Resnick. 2012. New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 annual meeting of the American Educational Research Association, Vancouver, Canada, Vol. 1. 25.Google ScholarGoogle Scholar
  7. John Brooke. 1996. SUS-A quick and dirty usability scale. Usability evaluation in industry 189, 194 (1996), 4--7.Google ScholarGoogle Scholar
  8. T. Carlson and J. del R. Millan. 2013. Brain-Controlled Wheelchairs: A Robotic Architecture. IEEE Robotics Automation Magazine 20, 1 (March 2013), 65--73. DOI:http://dx.doi.org/10.1109/MRA.2012.2229936Google ScholarGoogle ScholarCross RefCross Ref
  9. G. Chanel, C. Rebetez, M. Bétrancourt, and T. Pun. 2011. Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 41, 6 (Nov 2011), 1052--1063. DOI: http://dx.doi.org/10.1109/TSMCA.2011.2116000Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Daniel Chen and Roel Vertegaal. 2004. Using mental load for managing interruptions in physiologically attentive user interfaces. In CHI'04 extended abstracts on Human factors in computing systems. ACM, 1513--1516.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Fu-Yin Cherng, Wen-Chieh Lin, Jung-Tai King, and Yi-Chen Lee. 2016. An EEG-based approach for evaluating graphic icons from the perspective of semantic distance. In Proceedings of the 2016 CHI conference on human factors in computing systems. ACM, 4378--4389.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Neuromore Co. 2020. Neuromore. Software. (11 February 2020). Retrieved February 11, 2020 from https://www.neuromore.com/.Google ScholarGoogle Scholar
  13. Deborah R. Compeau and Christopher A. Higgins. 1995. Computer self-efficacy: Development of a measure and initial test. MIS quarterly (1995), 189--211.Google ScholarGoogle Scholar
  14. Chris S Crawford and Juan E Gilbert. 2019. Brains and Blocks: Introducing Novice Programmers to Brain-Computer Interface Application Development. ACM Transactions on Computing Education (TOCE) 19, 4 (2019), 39.Google ScholarGoogle Scholar
  15. Edward Cutrell and Desney Tan. 2008. BCI for passive input in HCI. In Proceedings of CHI, Vol. 8. Citeseer, 1--3.Google ScholarGoogle Scholar
  16. Sayamindu Dasgupta and Benjamin Mako Hill. 2018. How "wide walls" can increase engagement: evidence from a natural experiment in Scratch. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 361.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. N. Fraser. 2013. Blockly: A visual programming editor. Published.Google, Place (2013).Google ScholarGoogle Scholar
  18. Jérémy Frey, Maxime Daniel, Julien Castet, Martin Hachet, and Fabien Lotte. 2016. Framework for electroencephalography-based evaluation of user experience. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 2283--2294.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Jérémy Frey, Renaud Gervais, Stéphanie Fleck, Fabien Lotte, and Martin Hachet. 2014. Teegi: tangible EEG interface. In Proceedings of the 27th annual ACM symposium on User interface software and technology. ACM, 301--308.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Lukas Gehrke, Sezen Akman, Pedro Lopes, Albert Chen, Avinash Kumar Singh, Hsiang-Ting Chen, Chin-Teng Lin, and Klaus Gramann. 2019. Detecting Visuo-Haptic Mismatches in Virtual Reality using the Prediction Error Negativity of Event-Related Brain Potentials. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, 427.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Renaud Gervais, Jérémy Frey, Alexis Gay, Fabien Lotte, and Martin Hachet. 2016. Tobe: Tangible out-of-body experience. In Proceedings of the TEI'16: Tenth International Conference on Tangible, Embedded, and Embodied Interaction. ACM, 227--235.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. David Grimes, Desney S Tan, Scott E Hudson, Pradeep Shenoy, and Rajesh PN Rao. 2008. Feasibility and pragmatics of classifying working memory load with an electroencephalograph. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 835--844.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Mariam Hassib, Max Pfeiffer, Stefan Schneegass, Michael Rohs, and Florian Alt. 2017a. Emotion actuator: Embodied emotional feedback through electroencephalography and electrical muscle stimulation. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 6133--6146.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Mariam Hassib, Stefan Schneegass, Philipp Eiglsperger, Niels Henze, Albrecht Schmidt, and Florian Alt. 2017b. EngageMeter: A system for implicit audience engagement sensing using electroencephalography. In Proceedings of the 2017 chi conference on human factors in computing systems. ACM, 5114--5119.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Jin Huang, Chun Yu, Yuntao Wang, Yuhang Zhao, Siqi Liu, Chou Mo, Jie Liu, Lie Zhang, and Yuanchun Shi. 2014a. FOCUS : Enhancing Children's Engagement in Reading by Using Contextual BCI Training Sessions. Proc. CHI 2014 (2014), 1905--1908. DOI: http://dx.doi.org/10.1145/2556288.2557339Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Changing Minds: Exploring Brain-Computer Interface Experiences with High School Students

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems
      April 2020
      4474 pages
      ISBN:9781450368193
      DOI:10.1145/3334480

      Copyright © 2020 Owner/Author

      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 25 April 2020

      Check for updates

      Qualifiers

      • abstract

      Acceptance Rates

      Overall Acceptance Rate6,164of23,696submissions,26%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format .

    View HTML Format