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Richter CG, Li CM, Turnbull A, Haft SL, Schneider D, Luo J, Lima DP, Lin FV, Davidson RJ, Hoeft F. Brain imaging studies of emotional well-being: a scoping review. Front Psychol 2024; 14:1328523. [PMID: 38250108 PMCID: PMC10799564 DOI: 10.3389/fpsyg.2023.1328523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 12/06/2023] [Indexed: 01/23/2024] Open
Abstract
This scoping review provides an overview of previous empirical studies that used brain imaging techniques to investigate the neural correlates of emotional well-being (EWB). We compiled evidence on this topic into one accessible and usable document as a foundation for future research into the relationship between EWB and the brain. PRISMA 2020 guidelines were followed. We located relevant articles by searching five electronic databases with 95 studies meeting our inclusion criteria. We explored EWB measures, brain imaging modalities, research designs, populations studied, and approaches that are currently in use to characterize and understand EWB across the literature. Of the key concepts related to EWB, the vast majority of studies investigated positive affect and life satisfaction, followed by sense of meaning, goal pursuit, and quality of life. The majority of studies used functional MRI, followed by EEG and event-related potential-based EEG to study the neural basis of EWB (predominantly experienced affect, affective perception, reward, and emotion regulation). It is notable that positive affect and life satisfaction have been studied significantly more often than the other three aspects of EWB (i.e., sense of meaning, goal pursuit, and quality of life). Our findings suggest that future studies should investigate EWB in more diverse samples, especially in children, individuals with clinical disorders, and individuals from various geographic locations. Future directions and theoretical implications are discussed, including the need for more longitudinal studies with ecologically valid measures that incorporate multi-level approaches allowing researchers to better investigate and evaluate the relationships among behavioral, environmental, and neural factors. Systematic review registration https://osf.io/t9cf6/.
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Affiliation(s)
- Caroline G. Richter
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, United States
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, United States
| | - Celine Mylx Li
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, United States
- Department of Psychology, University of California, Berkeley, Berkeley, CA, United States
| | - Adam Turnbull
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, United States
- CogT Lab, Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, United States
| | - Stephanie L. Haft
- Department of Psychology, University of California, Berkeley, Berkeley, CA, United States
| | - Deborah Schneider
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, United States
| | - Jie Luo
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, United States
| | - Denise Pinheiro Lima
- Intensive Care Pediatrician, Pediatric Intensive Care Unit, Hospital Moinhos de Vento, Porto Alegre, Brazil
| | - Feng Vankee Lin
- CogT Lab, Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, United States
| | - Richard J. Davidson
- Center for Healthy Minds, University of Wisconsin, Madison, WI, United States
- Department of Psychology, University of Wisconsin, Madison, WI, United States
- Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin, Madison, WI, United States
- Department of Psychiatry, University of Wisconsin, Madison, WI, United States
| | - Fumiko Hoeft
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, United States
- Haskins Laboratories, New Haven, CT, United States
- Brain Imaging Research Center (BIRC), University of Connecticut, Storrs, CT, United States
- Department of Psychiatry and Behavioral Sciences, and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States
- Department of Neuropsychiatry, Keio University School of Medicine, Shinanomachi Shinjuku Tokyo, Tokyo, Japan
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Sharma S, Dubey AK, Ranjan P. Affective Video Tagging Framework using Human Attention Modelling through EEG Signals. INTERNATIONAL JOURNAL OF INTELLIGENT INFORMATION TECHNOLOGIES 2022. [DOI: 10.4018/ijiit.306968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The explosion of multimedia content over the past years is not surprising, thus their efficient management and analysis methods are always in demand. The affectiveness of any multimedia content deals with analyzing human perception and cognition while watching it. Human attention is also one of the important parameters, as it describes the engagement and interestingness of the user while watching that content. Considering this aspect, a video tagging framework is proposed in which the EEG signals of participants are used to analyze human perception while watching videos. A rigorous analysis has been performed on different scalp locations and frequency rhythms of brain signals to formulate significant features corresponding to affective and interesting video content. The analysis presented in this paper shows that the extracted human attention-based features are generating promising results with the accuracy of 93.2% using SVM-based classification model which supports the applicability of the model for various BCI-based applications for automatic classification of multimedia content.
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Affiliation(s)
- Shanu Sharma
- Amity School of Engineering and Technology, Amity University, Noida, India
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Masood N, Farooq H. EEG electrodes selection for emotion recognition independent of stimulus presentation paradigms. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-201779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Most of the electroencephalography (EEG) based emotion recognition systems rely on single stimulus to evoke emotions. EEG data is mostly recorded with higher number of electrodes that can lead to data redundancy and longer experimental setup time. The question “whether the configuration with lesser number of electrodes is common amongst different stimuli presentation paradigms” remains unanswered. There are publicly available datasets for EEG based human emotional states recognition. Since this work is focused towards classifying emotions while subjects are experiencing different stimuli, therefore we need to perform new experiments. Keeping aforementioned issues in consideration, this work presents a novel experimental study that records EEG data for three different human emotional states evoked with four different stimuli presentation paradigms. A methodology based on iterative Genetic Algorithm in combination with majority voting has been used to achieve configuration with reduced number of EEG electrodes keeping in consideration minimum loss of classification accuracy. The results obtained are comparable with recent studies. Stimulus independent configurations with lesser number of electrodes lead towards low computational complexity as well as reduced set up time for future EEG based smart systems for emotions recognition
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Affiliation(s)
- Naveen Masood
- Electrical Engineering Department, BahriaUniversity, Karachi, Pakistan
| | - Humera Farooq
- Computer Science Department, Bahria University, Karachi, Pakistan
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