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El Basbasse Y, Packheiser J, Peterburs J, Maymon C, Güntürkün O, Grimshaw G, Ocklenburg S. Walk the plank! Using mobile electroencephalography to investigate emotional lateralization of immersive fear in virtual reality. ROYAL SOCIETY OPEN SCIENCE 2023; 10:221239. [PMID: 37266038 PMCID: PMC10230188 DOI: 10.1098/rsos.221239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 04/03/2023] [Indexed: 06/03/2023]
Abstract
Most studies on emotion processing induce emotions through images or films. However, this method lacks ecological validity, limiting generalization to real-life emotion processing. More realistic paradigms using virtual reality (VR) may be better suited to investigate authentic emotional states and their neuronal correlates. This pre-registered study examines the neuronal underpinnings of naturalistic fear, measured using mobile electroencephalography (EEG). Seventy-five healthy participants walked across a virtual plank which extended from the side of a skyscraper-either 80 storeys up (the negative condition) or at street level (the neutral condition). Subjective ratings showed that the negative condition induced feelings of fear. Following the VR experience, participants passively viewed negative and neutral images from the international affective picture system (IAPS) outside of VR. We compared frontal alpha asymmetry between the plank and IAPS task and across valence of the conditions. Asymmetry indices in the plank task revealed greater right-hemispheric lateralization during the negative VR condition, relative to the neutral VR condition and to IAPS viewing. Within the IAPS task, no significant asymmetries were detected. In summary, our findings indicate that immersive technologies such as VR can advance emotion research by providing more ecologically valid ways to induce emotion.
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Affiliation(s)
- Yasmin El Basbasse
- Department of Biopsychology, Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr-University Bochum, Universitätsstrasse 150, 44780 Bochum, Germany
| | - Julian Packheiser
- Netherlands Institute for Neuroscience, Social Brain Lab, 1105 BA Amsterdam, The Netherlands
| | - Jutta Peterburs
- Institute for Systems Medicine & Department of Human Medicine, MSH Medical School Hamburg, Victoria University of Wellington, Wellington 6140, New Zealand
| | - Christopher Maymon
- School of Psychology, Victoria University of Wellington, Wellington 6140, New Zealand
| | - Onur Güntürkün
- Department of Biopsychology, Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr-University Bochum, Universitätsstrasse 150, 44780 Bochum, Germany
- Research Center One Health Ruhr, Research Alliance Ruhr, Ruhr University Bochum, Bochum, Germany
| | - Gina Grimshaw
- School of Psychology, Victoria University of Wellington, Wellington 6140, New Zealand
| | - Sebastian Ocklenburg
- Department of Biopsychology, Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr-University Bochum, Universitätsstrasse 150, 44780 Bochum, Germany
- Department of Psychology, MSH Medical School Hamburg, Am Kaiserkai 1, 20457 Hamburg, Germany
- Institute for Cognitive and Affective Neuroscience, Medical School Hamburg, Am Kaiserkai 1, 20457 Hamburg, Germany
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Deng Y, Wang Y, Xu L, Meng X, Wang L. Do you like it or not? Identifying preference using an electroencephalogram during the viewing of short videos. Psych J 2023. [PMID: 37186458 DOI: 10.1002/pchj.645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 02/08/2023] [Indexed: 05/17/2023]
Abstract
Accurately predicting whether a short video will be liked by viewers is a topic of interest to media researchers. This study used an electroencephalogram (EEG) to record neural activity in 109 participants as they watched short videos (16 clips per person) to see which neural signals reflected viewers' preferences. The results showed that, compared with the short videos they disliked, individuals would experience positive emotions [indexed by a higher theta power, lower (beta - theta)/(beta + theta) score], more relaxed states (indexed by a lower beta power), lower levels of mental engagement and alertness [indexed by a lower beta/(alpha + theta) score], and devote more attention (indexed by lower alpha/theta) when watching short videos they liked. We further used artificial neural networks to classify the neural signals of different preferences induced by short videos. The classification accuracy was the highest when using data from bands over the whole brain, which was 75.78%. These results may indicate the potential of EEG measurement to evaluate the subjective preferences of individuals for short videos.
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Affiliation(s)
- Yaling Deng
- State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China
- Neuroscience and Intelligent Media Institute, Communication University of China, Beijing, China
| | - Ye Wang
- State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China
- Neuroscience and Intelligent Media Institute, Communication University of China, Beijing, China
| | - Liming Xu
- School of Journalism, Communication University of China, Beijing, China
| | - Xiangli Meng
- School of International Studies, Communication University of China, Beijing, China
| | - Lingxiao Wang
- School of Animation and Digital Art, Communication University of China, Beijing, China
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Xing X, Li Z, Xu T, Shu L, Hu B, Xu X. SAE+LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG. Front Neurorobot 2019; 13:37. [PMID: 31244638 PMCID: PMC6581731 DOI: 10.3389/fnbot.2019.00037] [Citation(s) in RCA: 100] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 05/24/2019] [Indexed: 11/29/2022] Open
Abstract
EEG-based automatic emotion recognition can help brain-inspired robots in improving their interactions with humans. This paper presents a novel framework for emotion recognition using multi-channel electroencephalogram (EEG). The framework consists of a linear EEG mixing model and an emotion timing model. Our proposed framework considerably decomposes the EEG source signals from the collected EEG signals and improves classification accuracy by using the context correlations of the EEG feature sequences. Specially, Stack AutoEncoder (SAE) is used to build and solve the linear EEG mixing model and the emotion timing model is based on the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). The framework was implemented on the DEAP dataset for an emotion recognition experiment, where the mean accuracy of emotion recognition achieved 81.10% in valence and 74.38% in arousal, and the effectiveness of our framework was verified. Our framework exhibited a better performance in emotion recognition using multi-channel EEG than the compared conventional approaches in the experiments.
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Affiliation(s)
- Xiaofen Xing
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Zhenqi Li
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Tianyuan Xu
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Lin Shu
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xiangmin Xu
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
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Antismoking Campaigns' Perception and Gender Differences: A Comparison among EEG Indices. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2019; 2019:7348795. [PMID: 31143204 PMCID: PMC6501276 DOI: 10.1155/2019/7348795] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 03/24/2019] [Indexed: 12/22/2022]
Abstract
Human factors' aim is to understand and evaluate the interactions between people and tasks, technologies, and environment. Among human factors, it is possible then to include the subjective reaction to external stimuli, due to individual's characteristics and states of mind. These processes are also involved in the perception of antismoking public service announcements (PSAs), the main tool for governments to contrast the first cause of preventable deaths in the world: tobacco addiction. In the light of that, in the present article, it has been investigated through the comparison of different electroencephalographic (EEG) indices a typical item known to be able of influencing PSA perception, that is gender. In order to investigate the neurophysiological underpinnings of such different perception, we tested two PSAs: one with a female character and one with a male character. Furthermore, the experimental sample was divided into men and women, as well as smokers and nonsmokers. The employed EEG indices were the mental engagement (ME: the ratio between beta activity and the sum of alpha and theta activity); the approach/withdrawal (AW: the frontal alpha asymmetry in the alpha band); and the frontal theta activity and the spectral asymmetry index (SASI: the ratio between beta minus theta and beta plus theta). Results suggested that the ME and the AW presented an opposite trend, with smokers showing higher ME and lower AW than nonsmokers. The ME and the frontal theta also evidenced a statistically significant interaction between the kind of the PSA and the gender of the observers; specifically, women showed higher ME and frontal theta activity for the male character PSA. This study then supports the usefulness of the ME and frontal theta for purposes of PSAs targeting on the basis of gender issues and of the ME and the AW and for purposes of PSAs targeting on the basis of smoking habits.
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