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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.


Protocol design, including setting the baseline of the fNIRS system, as well as windowing the data collection during the View and NF epochs to account for the delay in hemodynamic response.
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Figure 4: Protocol design, including setting the baseline of the fNIRS system, as well as windowing the data collection during the View and NF epochs to account for the delay in hemodynamic response.

Mentions: The collection of fNIRS data was organized into blocks, each consisting of a sequence of epochs (short time periods with a specific task). We describe the structure of blocks and the experimental tasks during each epoch in the next section. HbO values on each channel were extracted using time synchronization markers. To compensate for the approximately 7 s delay in the hemodynamic response (Bunce et al., 2006), we excluded data from the first 7 s in each epoch and included data from the first 7 s after the completion of each epoch (Figure 4; Aranyi et al., 2015a).


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)

Protocol design, including setting the baseline of the fNIRS system, as well as windowing the data collection during the View and NF epochs to account for the delay in hemodynamic response.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 4: Protocol design, including setting the baseline of the fNIRS system, as well as windowing the data collection during the View and NF epochs to account for the delay in hemodynamic response.
Mentions: The collection of fNIRS data was organized into blocks, each consisting of a sequence of epochs (short time periods with a specific task). We describe the structure of blocks and the experimental tasks during each epoch in the next section. HbO values on each channel were extracted using time synchronization markers. To compensate for the approximately 7 s delay in the hemodynamic response (Bunce et al., 2006), we excluded data from the first 7 s in each epoch and included data from the first 7 s after the completion of each epoch (Figure 4; Aranyi et al., 2015a).

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.