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Conference Paper

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

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

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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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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;

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Gaebler,  Michael
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

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.


Cite as: http://hdl.handle.net/21.11116/0000-0002-CEAC-5
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.