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  Decoding subjective emotional arousal during a naturalistic VR experience from EEG using LSTMs

Hofmann, S., Klotzsche, F., Mariola, A., Nikulin, V. V., Villringer, A., & Gaebler, M. (2018). Decoding subjective emotional arousal during a naturalistic VR experience from EEG using LSTMs. In 2018 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR). doi:10.1109/AIVR.2018.00026.

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Hofmann et al - Decoding subjective emotional arousal during a naturalistic VR experience from EEG using LSTMs - IEEE AIVR, 2018.pdf (Publisher version), 811KB
 
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Hofmann et al - Decoding subjective emotional arousal during a naturalistic VR experience from EEG using LSTMs - IEEE AIVR, 2018.pdf
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 Creators:
Hofmann, Simon1, 2, Author                 
Klotzsche, Felix2, 3, Author           
Mariola, Alberto3, 4, Author
Nikulin, Vadim V.2, Author           
Villringer, Arno2, Author           
Gaebler, Michael2, Author           
Affiliations:
1Amsterdam Brain and Cognition, University of Amsterdam, the Netherlands, ou_persistent22              
2Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
3Berlin School of Mind and Brain, Humboldt University Berlin, Germany, ou_persistent22              
4Neuroscience Sussex, University of Sussex, United Kingdom, ou_persistent22              

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Free keywords: subjective experience, neural decoding, emotional arousal, continuous time series, naturalistic research designs
 Abstract: Emotional arousal (EA) denotes a heightened
state of activation that has both subjective and physiological aspects. The neurophysiology of subjective EA, among other mind-brain-body phenomena, can best be tested when subjects
are stimulated in a natural fashion. Immersive virtual reality (VR) enables naturalistic experimental stimulation and thus promises to increase the ecological validity of research findings i.e., how well they generalize to real-life settings. In this study, 45 participants experienced virtual rollercoaster rides while
their brain activity was recorded using electroencephalography (EEG). A Long Short-Term Memory (LSTM) recurrent neural network (RNN) was then trained on the alpha-frequency (8-12 Hz) component of the EEG signal (input) and the retrospectively acquired continuous reports of subjective EA (target). With the LSTM-based model, subjective EA could be
predicted significantly above chance level. This demonstrates a novel EEG-based decoding approach for subjective states of experience in naturalistic research designs using VR.

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Language(s): eng - English
 Dates: 2018-10-312018-07-312018-10-012018-12-102018-12-10
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1109/AIVR.2018.00026
 Degree: -

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Title: IEEE Artificial Intelligence and Virtual Reality
Place of Event: Taichung, Taiwan
Start-/End Date: 2018-12-10 - 2018-12-12
Invited: Yes

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Title: 2018 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: ISBN: 978-1-5386-9269-1