skip to main content
10.1145/3267305.3274124acmconferencesArticle/Chapter ViewAbstractPublication PagesubicompConference Proceedingsconference-collections
research-article

AttentivU: Evaluating the Feasibility of Biofeedback Glasses to Monitor and Improve Attention

Published:08 October 2018Publication History

ABSTRACT

Our everyday work is becoming increasingly complex and cognitively demanding. What we pay attention to during our day influences how effectively our brain prepares itself for action, and how much effort we apply to a task. To address this issue we present AttentivU -a system that uses wearable electroencephalography (EEG) to measure the attention of a person in realtime. When the user's attention level is low, the system provides real-time, subtle, haptic or audio feedback to nudge the person to become attentive again. We tested a first version of the system, which uses an EEG headband on 48 adults over several sessions in both a lab and classroom setting. The results show that the biofeedback redirects the attention of the participants to the task at hand and improves their performance on comprehension tests. We next tested the same approach in the form of glasses on 6 adults in a lab setting, as the glasses form factor may be more acceptable in the long run. We conclude with a discussion of an improved third version of AttentivU, currently under development, which combines a custom-made solution of the glasses form-factor with built-in electrooculography (EOG) and EEG electrodes as well as auditory feedback.

References

  1. M. Andujar and J 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 (CHI EA '13). ACM, New York, NY, USA, 703--708. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. C. Berka, D. J Levendowski, M.N Lumicao, A. Yau, G. Davis, V.T Zivkovic, R.E Olmstead, P.D Tremoulet and P.L Craven. 2007. EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviation, space, and environmental medicine 78, Supplement 1 (2007), B231-B244.Google ScholarGoogle Scholar
  3. W. Boucsein, A. Haarmannand and F. Schaefer. Combining skin conductance and heart rate variability for adaptive automation during simulated ifr flight. In Engineering Psychology and Cognitive Ergonomics, vol. 4562. 2007, 639--647. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. E. A., Byrne and R. Parasuraman. Psychophysiology and adaptive automation. Biological Psychology 42, 3 (1996), 249--268.Google ScholarGoogle ScholarCross RefCross Ref
  5. E. Cutrell and D.Tan. BCI for passive input in HCI. In Proc CHI'07 (2007).Google ScholarGoogle Scholar
  6. S. Dikker, L. Wan, I. Davidesco, L. Kaggen, M. Oostrik, J. McClintock, J. Rowland, G. Michalareas, J.J Van Bavel, M. Ding and D. Poeppel. (2017). Brain-to-Brain Synchrony Tracks Real-World Dynamic Group Interactions in the Classroom. Current Biology, Volume 27, Issue 9, 1375--1380.Google ScholarGoogle ScholarCross RefCross Ref
  7. F. G. Freeman, P. J. Mikulka, L. J. Prinzel and M. W. Scerbo. Evaluation of an adaptive automation system using three eeg indices with a visual tracking task. Biological Psychology 50, 1 (1999), 61--76.Google ScholarGoogle ScholarCross RefCross Ref
  8. M. Hassib, S. Schneegass, P. Eiglsperger, N. Henze, A. Schmidt and F. Alt. EngageMeter: A System for Implicit Audience Engagement Sensing Using Electroencephalography In CHI '17: Proceedings of the 34th SIGCHI Conference on Human Factors in Computing Systems. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. Huang, C. Yu, Y. Wang, Y. Zhao, S. Liu, C. Mo, J. Liu, L. Zhang and Y. Shi. 2014. FOCUS: enhancing children's engagement in reading by using contextual BCI training sessions. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '14). ACM, New York, NY, USA, 1905--1908. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. M. Marchesi and B. Riccò. 2013. BRAVO: A Brain Virtual Operator for Education Exploiting Brain-computer Interfaces. In CHI '13 Extended Abstracts on Human Factors in Computing Systems (CHI EA '13). ACM, New York, NY, USA, 3091--3094. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. A. T. Pope, E.H.Bogart and D. S. Bartolome. 1995. Biocybernetic system evaluates indices of operator engagement in automated task. Biological psychology 40, 1 (1995), 187--195.Google ScholarGoogle Scholar
  12. D. Szafir and B. Mutlu. 2013. ARTFul: Adaptive Review Technology for Flipped Learning. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '13). ACM, New York, NY, USA, 1001--1010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. C.T. Vi, J. Alexander, P.Irani, B. Babaee and S. Subramanian. 2014. Quantifying EEG Measured Task Engagement for use in Gaming Applications.Google ScholarGoogle Scholar
  14. T. O. Zander, C. Kothe, S. Jatzev and M. Gaertner. 2010. Enhancing human-computer interaction with input from active and passive brain-computer interfaces. In Brain-Comp Int. 2010, 181--199.Google ScholarGoogle Scholar

Index Terms

  1. AttentivU: Evaluating the Feasibility of Biofeedback Glasses to Monitor and Improve Attention

    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
      UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers
      October 2018
      1881 pages
      ISBN:9781450359665
      DOI:10.1145/3267305

      Copyright © 2018 ACM

      Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 8 October 2018

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited

      Acceptance Rates

      Overall Acceptance Rate764of2,912submissions,26%

      Upcoming Conference

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader