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Neurophysiophenomenology – predicting emotional arousal from brain arousal in a virtual reality roller coaster

MPS-Authors
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Klotzsche,  Felix
Berlin School of Mind and Brain, Humboldt University Berlin, Germany;
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Hofmann,  Simon
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Amsterdam Brain and Cognition (ABC), University of Amsterdam, the Netherlands;

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Nikulin,  Vadim V.
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Villringer,  Arno
Berlin School of Mind and Brain, Humboldt University Berlin, Germany;
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Gaebler,  Michael
Berlin School of Mind and Brain, Humboldt University Berlin, Germany;
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Mariola, A., Klotzsche, F., Hofmann, S., Nikulin, V. V., Villringer, A., & Gaebler, M. (2018). Neurophysiophenomenology – predicting emotional arousal from brain arousal in a virtual reality roller coaster. Poster presented at MindBrainBody Symposium, Berlin, Germany.


Cite as: http://hdl.handle.net/21.11116/0000-0004-F056-C
Abstract
Arousal is a core affect constituted of both bodily and subjective states that prepares an agent to respond to events of the natural environment. While the peripheral physiological components of arousal have been examined also under naturalistic conditions, its neural correlates were suggested mainly on the basis of simplifed experimental designs.   We used virtual reality (VR) to present a highly immersive and contextually rich scenario of roller coaster rides to evoke naturalistic states of emotional arousal. Simultaneously, we recorded EEG to validate the suggested neural correlates of arousal in alpha frequency oscillations (8-12Hz) over temporo-parietal cortical areas. To fnd the complex link between these alpha components and the participants’ continuous subjective reports of arousal, we employed a set of complementary analytical methods coming from machine learning and deep learning.