Decoding Subjective Emotional Arousal during a Naturalistic VR Experience from EEG Using LSTMs

Hofmann, Steffen; Klotzsche, Felix; Mariola, Alberto; Nikulin, Vadim V.; Villringer, Arno; Gaebler, Michael

doi:10.1109/aivr.2018.00026

Cited by 22 publications

(12 citation statements)

References 21 publications

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“…Hence, classifiers from this category usually distinguished high and low levels of arousal or positive and negative valence. Arousal inducing scenes often comprise a virtual roller-coaster ride (Hofmann et al, 2018;Teo and Chia, 2018;Bilgin et al, 2019) or dynamic mini-games (Shumailov and Gunes, 2017;. Emotional scenes are often used to manipulate the valence of people (Shumailov and Gunes, 2017;Mavridou et al, 2018b;Zheng et al, 2020).…”

Section: Arousal and Valencementioning

confidence: 99%

A Systematic Review of Physiological Measurements, Factors, Methods, and Applications in Virtual Reality

Halbig

Latoschik

2021

Front. Virtual Real.

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Measurements of physiological parameters provide an objective, often non-intrusive, and (at least semi-)automatic evaluation and utilization of user behavior. In addition, specific hardware devices of Virtual Reality (VR) often ship with built-in sensors, i.e. eye-tracking and movements sensors. Hence, the combination of physiological measurements and VR applications seems promising. Several approaches have investigated the applicability and benefits of this combination for various fields of applications. However, the range of possible application fields, coupled with potentially useful and beneficial physiological parameters, types of sensor, target variables and factors, and analysis approaches and techniques is manifold. This article provides a systematic overview and an extensive state-of-the-art review of the usage of physiological measurements in VR. We identified 1,119 works that make use of physiological measurements in VR. Within these, we identified 32 approaches that focus on the classification of characteristics of experience, common in VR applications. The first part of this review categorizes the 1,119 works by field of application, i.e. therapy, training, entertainment, and communication and interaction, as well as by the specific target factors and variables measured by the physiological parameters. An additional category summarizes general VR approaches applicable to all specific fields of application since they target typical VR qualities. In the second part of this review, we analyze the target factors and variables regarding the respective methods used for an automatic analysis and, potentially, classification. For example, we highlight which measurement setups have been proven to be sensitive enough to distinguish different levels of arousal, valence, anxiety, stress, or cognitive workload in the virtual realm. This work may prove useful for all researchers wanting to use physiological data in VR and who want to have a good overview of prior approaches taken, their benefits and potential drawbacks.

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Section: Arousal and Valencementioning

confidence: 99%

A Systematic Review of Physiological Measurements, Factors, Methods, and Applications in Virtual Reality

Halbig

Latoschik

2021

Front. Virtual Real.

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“…GNeuroPhaty was a technology developed to measure electrodermal response in virtual reality . LSTM was developed to record state postural signal (Hofmann et al, 2018). Multimodal Biofeedback was designed to capture up to six biomedical signals: EMG, EEG, GSR, temperature, heart rate and respiratory rate simultaneously.…”

Section: Original Biometrics Technologiesmentioning

confidence: 99%

ConVRgence (VRIC) Virtual Reality International Conference Proceedings

Richir

2020

IJVR

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“…More recently, researchers are beginning to explore the latest neural network algorithms for EEG-based affective state decoding. There are studies using autoencoder [79], deep belief networks (DBNs) [61],deep recursive neural network (RNN) [80], and convolutional neural network (CNN) [81]. Comparable or slightly improved performance was obtained, as compared to the classical classification methods.…”

Section: Challenges For Eeg-based Affective Computingmentioning

confidence: 99%

Ten challenges for EEG-based affective computing

Chen

Wang

et al. 2019

Brain Science Advances

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The emerging field of affective computing focuses on enhancing computers’ ability to understand and appropriately respond to people’s affective states in human-computer interactions, and has revealed significant potential for a wide spectrum of applications. Recently, the electroencephalography (EEG) based affective computing has gained increasing interest for its good balance between mechanistic exploration and real-world practical application. The present work reviewed ten theoretical and operational challenges for the existing affective computing researches from an interdisciplinary perspective of information technology, psychology, and neuroscience. On the theoretical side, we suggest that researchers should be well aware of the limitations of the commonly used emotion models, and be cautious about the widely accepted assumptions on EEG-emotion relationships as well as the transferability of findings based on different research paradigms. On the practical side, we propose several operational recommendations for the challenges about data collection, feature extraction, model implementation, online system design, as well as the potential ethical issues. The present review is expected to contribute to an improved understanding of EEG-based affective computing and promote further applications.

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Decoding Subjective Emotional Arousal during a Naturalistic VR Experience from EEG Using LSTMs

Cited by 22 publications

References 21 publications

A Systematic Review of Physiological Measurements, Factors, Methods, and Applications in Virtual Reality

A Systematic Review of Physiological Measurements, Factors, Methods, and Applications in Virtual Reality

ConVRgence (VRIC) Virtual Reality International Conference Proceedings

Ten challenges for EEG-based affective computing

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