Advances in Physiological ComputingTowards BCI-Based Implicit Control in Human–Computer Interaction
Advances in Physiological Computing: Towards BCI-Based Implicit Control in Human–Computer...
Zander, Thorsten O.; Brönstrup, Jonas; Lorenz, Romy; Krol, Laurens R.
2014-03-27 00:00:00
[In this chapter a specific aspect of Physiological Computing, that of implicit Human–Computer Interaction, is defined and discussed. Implicit Interaction aims at controlling a computer system by behavioural or psychophysiological aspects of user state, independently of any intentionally communicated command. This introduces a new type of Human–Computer Interaction, which in contrast to most forms of interaction implemented nowadays, does not require the user to explicitly communicate with the machine. Users can focus on understanding the current state of the system and developing strategies for optimally reaching the goal of the given interaction. For example, the system can assess the user state by means of passive Brain-Computer Interfaces, which the user needs not even be aware of. Based on this information and the given context the system can adapt automatically to the current strategies of the user. In a first study, a proof of principle is given, by implementing an Implicit Interaction to guide simple cursor movements in a 2D grid to a target. The results of this study clearly indicate the high potential of Implicit Interaction and introduce a new bandwidth of applications for passive Brain-Computer Interfaces.]
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Advances in Physiological ComputingTowards BCI-Based Implicit Control in Human–Computer Interaction
[In this chapter a specific aspect of Physiological Computing, that of implicit Human–Computer Interaction, is defined and discussed. Implicit Interaction aims at controlling a computer system by behavioural or psychophysiological aspects of user state, independently of any intentionally communicated command. This introduces a new type of Human–Computer Interaction, which in contrast to most forms of interaction implemented nowadays, does not require the user to explicitly communicate with the machine. Users can focus on understanding the current state of the system and developing strategies for optimally reaching the goal of the given interaction. For example, the system can assess the user state by means of passive Brain-Computer Interfaces, which the user needs not even be aware of. Based on this information and the given context the system can adapt automatically to the current strategies of the user. In a first study, a proof of principle is given, by implementing an Implicit Interaction to guide simple cursor movements in a 2D grid to a target. The results of this study clearly indicate the high potential of Implicit Interaction and introduce a new bandwidth of applications for passive Brain-Computer Interfaces.]
Published: Mar 27, 2014
Keywords: Amyotrophic Lateral Sclerosis; User State; Motor Imagery; Technical System; Implicit Information
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