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Alberto Mariola

dblp:225/8292 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Wearable and physiological sensing · 100%
Computer graphics and multimedia
1 paper
Virtual and augmented reality · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wearable and physiological sensing
electroencephalography
0.312018
Using EEG to Decode Subjective Levels of Emotional Arousal During an Immersive VR Roller Coaster Ride · VR 2018
Virtual and augmented reality › virtual reality
VR experience
0.112018
Using EEG to Decode Subjective Levels of Emotional Arousal During an Immersive VR Roller Coaster Ride · VR 2018

Methods — techniques the papers use, named apart from their topics

spatial filtering · 0.7EEG classification · 0.7
YearPublicationVenuePosition
2018 Using EEG to Decode Subjective Levels of Emotional Arousal During an Immersive VR Roller Coaster Ride
abstract
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.
Felix Klotzsche, Alberto Mariola, Simon M. Hofmann, Vadim V. Nikulin, Arno Villringer, Michael Gaebler
VR2