Modeling user behavior data in systems of engagement

Bent, Oliver; Dey, Prasenjit; Weldemariam, Komminist; Mohania, Mukesh

doi:10.1016/j.future.2016.05.038

Cited by 11 publications

(1 citation statement)

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“…Artificial learning companions, which are animated conversational agents involved in an interaction with the user [11], could provide such social and emotional feedback. Physiological and neurophysiological data recordings offer the possibility to infer users' states/traits and to adapt the behavior of the companion accordingly [7,9]. The training would benefit from the later, for example the difficulty of the task could be modulated in order to keep the user motivated.…”

Section: Introductionmentioning

confidence: 99%

Towards Artificial Learning Companions for Mental Imagery-based Brain-Computer Interfaces

Pillette¹,

Jeunet²,

Nkambou³

et al. 2019

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Mental Imagery based Brain-Computer Interfaces (MI-BCI) enable their users to control an interface, e.g., a prosthesis, by performing mental imagery tasks only, such as imagining a right arm movement while their brain activity is measured and processed by the system. Designing and using a BCI requires users to learn how to produce different and stable patterns of brain activity for each of the mental imagery tasks. However, current training protocols do not enable every user to acquire the skills required to use BCIs. These training protocols are most likely one of the main reasons why BCIs remain not reliable enough for wider applications outside research laboratories. Learning companions have been shown to improve training in different disciplines, but they have barely been explored for BCIs so far. This article aims at investigating the potential benefits learning companions could bring to BCI training by improving the feedback, i.e., the information provided to the user, which is primordial to the learning process and yet have proven both theoretically and practically inadequate in BCI. This paper first presents the potentials of BCI and the limitations of current training approaches. Then, it reviews both the BCI and learning companion literature regarding three main characteristics of feedback: its appearance, its social and emotional components and its cognitive component. From these considerations, this paper draws some guidelines, identify open challenges and suggests potential solutions to design and use learning companions for BCIs.

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Section: Introductionmentioning

confidence: 99%