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Affective Interaction with a Virtual Character Through an fNIRS Brain-Computer Interface.

Aranyi G, Pecune F, Charles F, Pelachaud C, Cavazza M - Front Comput Neurosci (2016)

Bottom Line: We use for affective input the asymmetric activity in the dorsolateral prefrontal cortex (DL-PFC), which has been previously found to be related to the high-level affective-motivational dimension of approach/avoidance.We carried out an experiment with 18 subjects, which demonstrated that subjects are able to successfully engage with the virtual agent by controlling their mental disposition through NF, and that they perceived the agent's responses as realistic and consistent with their projected mental disposition.Overall, our contribution reconciles a model of affect derived from brain metabolic data with an ecologically valid, yet computationally controllable, virtual affective communication environment.

View Article: PubMed Central - PubMed

Affiliation: School of Computing, Teesside University Middlesbrough, UK.

ABSTRACT
Affective brain-computer interfaces (BCI) harness Neuroscience knowledge to develop affective interaction from first principles. In this article, we explore affective engagement with a virtual agent through Neurofeedback (NF). We report an experiment where subjects engage with a virtual agent by expressing positive attitudes towards her under a NF paradigm. We use for affective input the asymmetric activity in the dorsolateral prefrontal cortex (DL-PFC), which has been previously found to be related to the high-level affective-motivational dimension of approach/avoidance. The magnitude of left-asymmetric DL-PFC activity, measured using functional near infrared spectroscopy (fNIRS) and treated as a proxy for approach, is mapped onto a control mechanism for the virtual agent's facial expressions, in which action units (AUs) are activated through a neural network. We carried out an experiment with 18 subjects, which demonstrated that subjects are able to successfully engage with the virtual agent by controlling their mental disposition through NF, and that they perceived the agent's responses as realistic and consistent with their projected mental disposition. This interaction paradigm is particularly relevant in the case of affective BCI as it facilitates the volitional activation of specific areas normally not under conscious control. Overall, our contribution reconciles a model of affect derived from brain metabolic data with an ecologically valid, yet computationally controllable, virtual affective communication environment.

No MeSH data available.


Related in: MedlinePlus

Progression of the agent’s behaviors according to its level of engagement.
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Figure 2: Progression of the agent’s behaviors according to its level of engagement.

Mentions: To characterize the virtual agent’s range of non-verbal behaviors, we considered four levels of engagement, ranging from disengagement to full engagement. We designed non-verbal behaviors for our virtual agent corresponding to these four levels (see Figure 2). Gaze is one of the most prominent indicators of engagement. Indeed, gaze avoidance can be perceived as disengagement (Doherty-Sneddon et al., 2002), while mutual gaze is attributed to engagement (Burgoon et al., 1984). Moreover, empathic engagement can be achieved through affective interactions (Hall et al., 2005). Frequently expressing positively valenced emotions denote a high level of engagement, while negative expressions usually indicate disengagement.


Affective Interaction with a Virtual Character Through an fNIRS Brain-Computer Interface.

Aranyi G, Pecune F, Charles F, Pelachaud C, Cavazza M - Front Comput Neurosci (2016)

Progression of the agent’s behaviors according to its level of engagement.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4940367&req=5

Figure 2: Progression of the agent’s behaviors according to its level of engagement.
Mentions: To characterize the virtual agent’s range of non-verbal behaviors, we considered four levels of engagement, ranging from disengagement to full engagement. We designed non-verbal behaviors for our virtual agent corresponding to these four levels (see Figure 2). Gaze is one of the most prominent indicators of engagement. Indeed, gaze avoidance can be perceived as disengagement (Doherty-Sneddon et al., 2002), while mutual gaze is attributed to engagement (Burgoon et al., 1984). Moreover, empathic engagement can be achieved through affective interactions (Hall et al., 2005). Frequently expressing positively valenced emotions denote a high level of engagement, while negative expressions usually indicate disengagement.

Bottom Line: We use for affective input the asymmetric activity in the dorsolateral prefrontal cortex (DL-PFC), which has been previously found to be related to the high-level affective-motivational dimension of approach/avoidance.We carried out an experiment with 18 subjects, which demonstrated that subjects are able to successfully engage with the virtual agent by controlling their mental disposition through NF, and that they perceived the agent's responses as realistic and consistent with their projected mental disposition.Overall, our contribution reconciles a model of affect derived from brain metabolic data with an ecologically valid, yet computationally controllable, virtual affective communication environment.

View Article: PubMed Central - PubMed

Affiliation: School of Computing, Teesside University Middlesbrough, UK.

ABSTRACT
Affective brain-computer interfaces (BCI) harness Neuroscience knowledge to develop affective interaction from first principles. In this article, we explore affective engagement with a virtual agent through Neurofeedback (NF). We report an experiment where subjects engage with a virtual agent by expressing positive attitudes towards her under a NF paradigm. We use for affective input the asymmetric activity in the dorsolateral prefrontal cortex (DL-PFC), which has been previously found to be related to the high-level affective-motivational dimension of approach/avoidance. The magnitude of left-asymmetric DL-PFC activity, measured using functional near infrared spectroscopy (fNIRS) and treated as a proxy for approach, is mapped onto a control mechanism for the virtual agent's facial expressions, in which action units (AUs) are activated through a neural network. We carried out an experiment with 18 subjects, which demonstrated that subjects are able to successfully engage with the virtual agent by controlling their mental disposition through NF, and that they perceived the agent's responses as realistic and consistent with their projected mental disposition. This interaction paradigm is particularly relevant in the case of affective BCI as it facilitates the volitional activation of specific areas normally not under conscious control. Overall, our contribution reconciles a model of affect derived from brain metabolic data with an ecologically valid, yet computationally controllable, virtual affective communication environment.

No MeSH data available.


Related in: MedlinePlus