Johnny Chung Lee
Desney S. Tan
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.
Supplemental Material
Available for Download
Slides from the presentation
Supplemental material for Using a low-cost electroencephalograph for task classification in HCI research
- 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.Google Scholar
- Brainmaster. http://www.brainmaster.com.Google Scholar
- 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. Google ScholarDigital Library
- Coyle, S., Ward, T., & Markham, C. (2003). Brain-computer interfaces: A review. Interdisciplinary Science Reviews, 28(2), 112--118.Google ScholarCross Ref
- Cutmore, T. R. H, & James, D. A. (1999). Identifying and Reducing Noise in Physiological Recordings. International Journal of Psychophysiology, 32, 129--150.Google ScholarCross Ref
- Fayyad, U. M., & Irani, K. B. (1992). On the handling of continuous-valued attributes in decision tree generation. Machine Learning, 8, 87--102. Google ScholarCross Ref
- Fisch, B. J. (2005). Fisch & Spehlmann's EEG primer: Basic principles of digital and analog EEG. Amsterdam: Elsevier.Google Scholar
- 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.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- 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.Google Scholar
- Gevins, A. S., & Remond, A. (1987). Handbook of Electroencephalography and Clinical Neurophysiology: Methods of analysis of brain electrical and magnetic signals. Amsterdam: Elsevier.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- Kramer, A. F. (1991). Physiological metrics of mental workload: A review of recent progress. In Multiple Task Performance (ed. Damos, D.L.), 279--328.Google Scholar
- 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.Google ScholarCross Ref
- Millan, J. Adaptive brain interfaces. Communications of the ACM, 46(3), 74--80. Google ScholarDigital Library
- OpenEEG Project. http://openeeg.sourceforge.net.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- Smith, R. C. (2004). Electroencaphalograph based brain computer interfaces. Masters Dissertation, University College Dublin.Google Scholar
- 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. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques (2nd Edition), San Francisco: Morgan Kaufmann. Google ScholarDigital Library
- 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.Google ScholarCross Ref
Index Terms
- Using a low-cost electroencephalograph for task classification in HCI research
Recommendations
Distinguishing Difficulty Levels with Non-invasive Brain Activity Measurements
INTERACT '09: Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part IPassive brain-computer interfaces are designed to use brain activity as an additional input, allowing the adaptation of the interface in real time according to the user's mental state. The goal of the present study is to distinguish between different ...
Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain-computer interface
Motor imagery (MI) tasks classification provides an important basis for designing brain-computer interface (BCI) systems. If the MI tasks are reliably distinguished through identifying typical patterns in electroencephalography (EEG) data, a motor ...
EEG signals classification for brain computer interfaces based on Gaussian process classifier
ICICS'09: Proceedings of the 7th international conference on Information, communications and signal processingClassification of electroencephalogram (EEG) is a crucial issue for EEG-based brain computer interface (BCI) system. In this paper, the performances of the Gaussian process classifier (GPC) for three different categories of EEG signals, i.e. steady ...
Comments