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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.


Distribution of the effect-size measure r in successful blocks.
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Figure 7: Distribution of the effect-size measure r in successful blocks.

Mentions: The effect-size r is interpreted similarly to the correlation coefficient, its value is constrained between 0 and 1, and according to Cohen (1988) conventions, r = 0.10 corresponds to small, 0.30 to medium, and 0.50 to large effect-size. The smallest r we could detect was 0.18 (small), and average r in successful blocks was 0.75 (large). Furthermore, the distribution of r in successful blocks was negatively skewed (Median r = 0.81), indicating that successful blocks were predominantly characterized with large asymmetry increase (Figure 7).


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)

Distribution of the effect-size measure r in successful blocks.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 7: Distribution of the effect-size measure r in successful blocks.
Mentions: The effect-size r is interpreted similarly to the correlation coefficient, its value is constrained between 0 and 1, and according to Cohen (1988) conventions, r = 0.10 corresponds to small, 0.30 to medium, and 0.50 to large effect-size. The smallest r we could detect was 0.18 (small), and average r in successful blocks was 0.75 (large). Furthermore, the distribution of r in successful blocks was negatively skewed (Median r = 0.81), indicating that successful blocks were predominantly characterized with large asymmetry increase (Figure 7).

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.