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What You Expect Is What You Get? Potential Use of Contingent Negative Variation for Passive BCI Systems in Gaze-Based HCI

MPG-Autoren
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Zander,  TO
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Ihme, K., & Zander, T. (2011). What You Expect Is What You Get? Potential Use of Contingent Negative Variation for Passive BCI Systems in Gaze-Based HCI. In S. D’Mello, A. Graesser, B. Schuller, & J. martin (Eds.), Affective Computing and Intelligent Interaction (pp. 447-456). Berlin, Germany: Springer.


Zitierlink: http://hdl.handle.net/21.11116/0000-0001-F298-2
Zusammenfassung
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.