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Using EEG to decode subjective levels of emotional arousal during an immersive VR roller coaster ride

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

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

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

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フルテキスト (公開)

Klotzsche et al (2018)_IEEEVR18_poster.pdf
(ポストプリント), 777KB

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引用

Klotzsche, F., Mariola, A., Hofmann, S., Nikulin, V. V., Villringer, A., & Gaebler, M. (2018). Using EEG to decode subjective levels of emotional arousal during an immersive VR roller coaster ride. Poster presented at 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), Reutlingen, Germany.


引用: https://hdl.handle.net/21.11116/0000-0004-C44B-B
要旨
Emotional arousal is a key component of a user’s experience in immersive virtual reality (VR). Subjective and highly dynamic in nature, emotional arousal involves the whole body and particularly the brain. However, it has been difficult to relate subjective emotional arousal to an objective, neurophysiological marker—especially in naturalistic settings. We tested the association between continuously changing states of emotional arousal and oscillatory power in the brain during a VR roller coaster experience. We used novel spatial filtering approaches to predict self-reported emotional arousal from the electroencephalogram (EEG) signal of 38 participants. Periods of high vs. low emotional arousal could be classified with accuracies significantly above chance level. Our results are consistent with prior findings regarding emotional arousal in less naturalistic settings. We demonstrate a new approach to decode states of subjective emotional arousal from continuous EEG data in an immersive VR experience.