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Using a low-cost electroencephalograph for task classification in HCI research

Published: 15 October 2006 Publication History

Abstract

Modern brain sensing technologies provide a variety of methods for detecting specific forms of brain activity. In this paper, we present an initial step in exploring how these technologies may be used to perform task classification and applied in a relevant manner to HCI research. We describe two experiments showing successful classification between tasks using a low-cost off-the-shelf electroencephalograph (EEG) system. In the first study, we achieved a mean classification accuracy of 84.0% in subjects performing one of three cognitive tasks - rest, mental arithmetic, and mental rotation - while sitting in a controlled posture. In the second study, conducted in more ecologically valid setting for HCI research, we attained a mean classification accuracy of 92.4% using three tasks that included non-cognitive features: a relaxation task, playing a PC based game without opponents, and engaging opponents within the game. Throughout the paper, we provide lessons learned and discuss how HCI researchers may utilize these technologies in their work.

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References

[1]
Anderson, C. W., & Sijerččić, Z. (1996). Classification of EEG Signals from Four Subjects During Five Mental Tasks. Proceedings of the Conference on Engineering Applications in Neural Networks, 407--414.
[2]
Brainmaster. http://www.brainmaster.com.
[3]
Chen, D., & Vertegaal, R. (2004). Using mental load for managing interruptions in physiologically attentive user interfaces. Extended Abstracts of SIGCHI 2004 Conference on Human Factors in Computing Systems, 1513--1516.
[4]
Coyle, S., Ward, T., & Markham, C. (2003). Brain-computer interfaces: A review. Interdisciplinary Science Reviews, 28(2), 112--118.
[5]
Cutmore, T. R. H, & James, D. A. (1999). Identifying and Reducing Noise in Physiological Recordings. International Journal of Psychophysiology, 32, 129--150.
[6]
Fayyad, U. M., & Irani, K. B. (1992). On the handling of continuous-valued attributes in decision tree generation. Machine Learning, 8, 87--102.
[7]
Fisch, B. J. (2005). Fisch & Spehlmann's EEG primer: Basic principles of digital and analog EEG. Amsterdam: Elsevier.
[8]
Fitzgibbon, S. P., Pope, K. J., Mackenzie, L., Clark, C. R., & Willoughby, J. O. (2004). Cognitive tasks augment gamma EEG power. Clinical Neurophysiology, 115, 1802--1809.
[9]
Fogarty, J., Ko, A. J., Aung, H. H., Golden, E., Tang, K. P., & Hudson, S. E. (2005). Examining task engagement in sensor-based statistical models of human interruptibility. Proceedings of SIGCHI 2005 Conference on Human Factors in Computing Systems, 331--340.
[10]
Gevins, A., Leong, H., Du, R., Smith, M. E., Le, J., DuRousseau, D., Zhang, J., & Libove, J. (1995). Towards measurement of brain function in operational environments. Biological Physiology, 40, 169--186.
[11]
Gevins, A. S., & Remond, A. (1987). Handbook of Electroencephalography and Clinical Neurophysiology: Methods of analysis of brain electrical and magnetic signals. Amsterdam: Elsevier.
[12]
Gevins, A. S., Zeitlin, J. C., Doyle, J. C., Schaffer, R. E., & Callaway, E. (1979). EEG patterns during 'cognitive' tasks. II. Methodology and analysis of complex behaviors. Electroencephalography and Clinical Neurophysiology, 47, 704--710.
[13]
Keirn, Z. A., & Aunon, J. I. (1990). A new mode of communication between man and his surroundings. IEEE Transactions on Biomedical Engineering, 37(12), 1209--1214.
[14]
Kitamura, Y., Yamaguchi, Y., Imamizu, H., Kishino, F., & Kawato, M. (2003). Things happening in the brain while humans learn to use new tools. Proceedings of SIGCHI 2003 Conference on Human Factors in Computing Systems, 417--424.
[15]
Kramer, A. F. (1991). Physiological metrics of mental workload: A review of recent progress. In Multiple Task Performance (ed. Damos, D.L.), 279--328.
[16]
Mason, S. G., & Birch, G. E. (2003). A general framework for brain-computer interface design. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(1), 70--85.
[17]
Millan, J. Adaptive brain interfaces. Communications of the ACM, 46(3), 74--80.
[18]
OpenEEG Project. http://openeeg.sourceforge.net.
[19]
Palaniappan, R. (2005). Brain computer interface design using band powers extracted during mental tasks. Proceedings of the 2nd International IEEE EMBS Conference on Neural Engineering, 321--324.
[20]
Picton, T. W., Bentin, P., Berg, P., Hillyard, S. A., Johnson, J. R., Miller, G. A., et al. (2000). Guidelines for using human event-related potentials to study cognition: Recording standards and publication criteria. Psychophysiology, 37, 127--152.
[21]
Smith, R. C. (2004). Electroencaphalograph based brain computer interfaces. Masters Dissertation, University College Dublin.
[22]
Velichkovsky, B., & Hansen, J. P. (1996). New technological windows into mind: There is more in eyes and brains for human-computer interaction. Proceedings of the SIGCHI 1996 Conference on Human Factors in Computing Systems, 496--503.
[23]
van Boxtel, G. J. M. (1998). Computational and statistical methods for analyzing event-related potential data. Behavior Research Methods, Instruments, & Computers, 30(1), 87--102.
[24]
Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques (2nd Edition), San Francisco: Morgan Kaufmann.
[25]
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughn, T. M. (2002). Brain-computer interfaces for communication and control. Clinical Neurophysiology, 113, 767--791.

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cover image ACM Conferences
UIST '06: Proceedings of the 19th annual ACM symposium on User interface software and technology
October 2006
354 pages
ISBN:1595933131
DOI:10.1145/1166253
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]

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Publication History

Published: 15 October 2006

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Author Tags

  1. brain-computer interface
  2. electroencephalogram (EEG)
  3. human cognition
  4. physical artifacts
  5. task classification

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Overall Acceptance Rate 561 of 2,567 submissions, 22%

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  • (2024)Mental Workload Classification and Tasks Detection in Multitasking: Deep Learning Insights from EEG StudyBrain Sciences10.3390/brainsci1402014914:2(149)Online publication date: 31-Jan-2024
  • (2024)NeuroCHI: Are We Prepared for the Integration of the Brain with Computing?Extended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3643973(1-5)Online publication date: 11-May-2024
  • (2024)DBCIE: A Database for Brain-Computer Interface Using Electrophysiological Signal2024 International Conference on Signal Processing and Communications (SPCOM)10.1109/SPCOM60851.2024.10631606(1-5)Online publication date: 1-Jul-2024
  • (2023)The BciAi4SLA Project: Towards a User-Centered BCIElectronics10.3390/electronics1205123412:5(1234)Online publication date: 4-Mar-2023
  • (2023)Measuring Brain Activation Patterns from Raw Single-Channel EEG during Exergaming: A Pilot StudyElectronics10.3390/electronics1203062312:3(623)Online publication date: 26-Jan-2023
  • (2023)Human–Computer Interaction Multi-Task Modeling Based on Implicit Intent EEG DecodingApplied Sciences10.3390/app1401036814:1(368)Online publication date: 30-Dec-2023
  • (2023)Let's Make it Accessible: The Challenges Of Working With Low-cost Commercially Available Wearable DevicesProceedings of the 35th Australian Computer-Human Interaction Conference10.1145/3638380.3638415(493-503)Online publication date: 2-Dec-2023
  • (2023)A Survey on Measuring Cognitive Workload in Human-Computer InteractionACM Computing Surveys10.1145/358227255:13s(1-39)Online publication date: 13-Jul-2023
  • (2022)Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation AnalysisSensors10.3390/s2209316922:9(3169)Online publication date: 21-Apr-2022
  • (2022)Understanding HCI Practices and Challenges of Experiment Reporting with Brain Signals: Towards Reproducibility and ReuseACM Transactions on Computer-Human Interaction10.1145/349055429:4(1-43)Online publication date: 31-Mar-2022
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