Abstract
When using eye movements for cursor control in human-computer interaction (HCI), it may be difficult to find an appropriate substitute for the click operation. Most approaches make use of dwell times. However, in this context the so-called Midas-Touch-Problem occurs which means that the system wrongly interprets fixations due to long processing times or spontaneous dwellings of the user as command. Lately it has been shown that brain-computer interface (BCI) input bears good prospects to overcome this problem using imagined hand movements to elicit a selection. The current approach tries to develop this idea further by exploring potential signals for the use in a passive BCI, which would have the advantage that the brain signals used as input are generated automatically without conscious effort of the user. To explore event-related potentials (ERPs) giving information about the user’s intention to select an object, 32-channel electroencephalography (EEG) was recorded from ten participants interacting with a dwell-time-based system. Comparing ERP signals during the dwell time with those occurring during fixations on a neutral cross hair, a sustained negative slow cortical potential at central electrode sites was revealed. This negativity might be a contingent negative variation (CNV) reflecting the participants’ anticipation of the upcoming selection. Offline classification suggests that the CNV is detectable in single trial (mean accuracy 74.9 %). In future, research on the CNV should be accomplished to ensure its stable occurence in human-computer interaction and render possible its use as a potential substitue for the click operation.
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References
Prablanc, C., Echallier, J.F., Komilis, E., Jeannerod, M.: Optimal response of eye and hand motor systems in pointing at a visual target. I. Spatio-temporal characteristics of eye and hand movements and their relationships when varying the amount of visual information. Biological Cybernetics 35, 113–124
Jacob, R.J.K.: The use of eye movements in human-computer interaction techniques: what you look at is what you get. ACM Transactions on Information Systems 9(2), 152–169 (1991)
Jacob, R.J.K.: Hot topics-eye-gaze computer interfaces: what you look at is what you get. Computer 26(7), 65–66 (1993)
Velichkovsky, B.M., Hansen, J.P.: New technological windows into mind: there is more in eyes and brains for human-computer interaction. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems: Common Ground, pp. 496–503 (1996)
Yarbus, A.L.: Eye movements during perception of complex objects. Eye Movements and Vision 7, 171–196 (1967)
Farwell, L.A., Donchin, E.: Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology 70(6), 510–523 (1988)
Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain–computer interfaces for communication and control. Clinical Neurophysiology 133(6), 767–791 (2002)
Zander, T.O., Kothe, C.: Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general. Journal of Neural Engineering 8, 025005 (2011)
Zander, T.O., Kothe, C., Jatzev, S., Gaertner, M.: Enhancing human–computer interaction with input from active and passive brain–computer interfaces. In: Tan, D., Nijholt, A. (eds.) The Human in Brain–Computer Interfaces and the Brain in Human–Computer Interaction, pp. 24–29 (2010)
Zander, T.O., Gaertner, M., Kothe, C., Vilimek, R.: Combining Eye Gaze Input with a Brain-Computer Interface for Touchless Human-Computer Interaction. International Journal of Human-Computer Interaction 27(1), 38–51 (2011)
Oostenveld, R., Praamstra, P.: The five percent electrode system for high-resolution EEG and ERP measurements. Clinical Neurophysiology 112(4), 713–719 (2001)
Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods 134(1), 9–21 (2004)
Kothe, C.: Design and Implementation of a Research Brain-Computer Interface. Diploma’s Thesis, Berlin Technical University, Berlin, Germany (September 2009)
Delorme, A., Kothe, C., Vankov, A., Bigdely-Shamlo, N., Oostenveld, R., Zander, T.O., Makeig, S.: MATLAB-based tools for BCI research. In: Tan, Nijholt (eds.) (B+H)CI: Brain-Computer Interfaces Applying our Minds to Human-Computer Interaction, pp. 241–259. Springer, Berlin (2010)
Luck, S.J.: An introduction to the event-related potential technique. MIT Press, Cambridge (2005)
Blankertz, B., Curio, G., MĂĽller, K.R.: Classifying Single Trial EEG: Towards Brain Computer Interfacing. In: Advances in Neural Information Processing Systems: Proceedings of the 2002 Conference, vol. 157 (2002)
Friedman, J.H.: Regularized discriminant analysis. Journal of the American Statistical Association 84(405), 165–175 (1989)
Zander, T.O., Ihme, K., Gaertner, M., Roetting, M.: A public data hub for benchmarking common brain–computer interface algorithms. Journal of Neural Engineering 8, 025021 (2011)
Duda, R.O., Hart, P.E., Miley, J.: Pattern Classification, 2nd edn. Wiley Interscience, Hoboken (2001)
Müller-Putz, G.R., Scherer, R., Brunner, C., Leeb, R., Pfurtscheller, G.: Better than random? A closer look on BCI results. International Journal of Bioelectromagnetism 10(1), 52–55 (2008)
Walter, W.G.: Slow potential waves in the human brain associated with expectancy, attention and decision. European Archives of Psychiatry and Clinical Neuroscience 206(3), 309–322 (1964)
Ruchkin, D.S., Sutton, S., Mahaffey, D., Glaser, J.: Terminal CNV in the absence of motor response. Electroencephalography and Clinical Neurophysiology 63(5), 445–463 (1986)
Gaillard, A.W.K.: The late CNV wave: Preparation versus expectancy. Psychophysiology 14(6), 563–568 (1997)
Pfeuty, M., Ragot, R., Pouthas, V.: Relationship between CNV and timing of an upcoming event. Neuroscience Letters 382(1-2), 106–111 (2005)
Van Boxtel, G.J.M., Brunia, C.H.M.: Motor and non-motor aspects of slow brain potentials. Biological Psychology 38(1), 37–51 (1994)
Brunia, C.H.M., Van Boxtel, G.J.M.: Wait and see. International Journal of Psychophysiology 43(1), 59–71 (2001)
Nagai, Y., Critchley, H.D., Featherstone, E., Fenwick, P.B.C., Trimble, M.R., Dolan, R.J.: Brain activity relating to the contingent negative variation: an fMRI investigation. NeuroImage 21(4), 1232–1241 (2004)
Garipelli, G., Chavarriaga, R., del R Millan, J.: Single trial recognition of anticipatory slow cortical potentials: The role of spatio-spectral filtering. In: Proceedings of the 5th International IEEE/EMBS Conference on Neural Engineering (NER), Cancun, pp. 408–411 (2011)
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Ihme, K., Zander, T.O. (2011). What You Expect Is What You Get? Potential Use of Contingent Negative Variation for Passive BCI Systems in Gaze-Based HCI. In: D’Mello, S., Graesser, A., Schuller, B., Martin, JC. (eds) Affective Computing and Intelligent Interaction. ACII 2011. Lecture Notes in Computer Science, vol 6975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24571-8_57
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DOI: https://doi.org/10.1007/978-3-642-24571-8_57
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