Resilience predicts posttraumatic cognitions after a trauma reminder task and subsequent positive emotion induction among veterans with PTSD.

Electroencephalography (EEG) can assist with the detection of major depressive disorder (MDD). However, the ability to distinguish adults with MDD from healthy individuals using resting-state EEG features has reached a bottleneck. To address this limitation, we collected EEG data as participants engaged with positive pictures from the International Affective Picture System. Because MDD is associated with blunted positive emotions, we reasoned that this approach would yield highly dissimilar EEG features in healthy versus depressed adults. We extracted three types of relative EEG power features from different frequency bands (delta, theta, alpha, beta, and gamma) during the emotion task and resting state. We also applied a novel classifier, called a conformal kernel support vector machine (CK-SVM), to try to improve the generalization performance of conventional SVMs. We then compared CK-SVM performance with three machine learning classifiers: linear discriminant analysis (LDA), conventional SVM, and quadratic discriminant analysis. The results from the initial analyses using the LDA classifier on 55 participants (24 MDD, 31 healthy controls) showed that the participant-independent classification accuracy obtained by leave-one-participant-out cross-validation (LOPO-CV) was higher for the EEG recorded during the positive emotion induction versus the resting state for all types of relative EEG power. Furthermore, the CK-SVM classifier achieved higher LOPO-CV accuracy than the other classifiers. The best accuracy (83.64%; sensitivity = 87.50%, specificity = 80.65%) was achieved by the CK-SVM, using seven relative power features extracted from seven electrodes. Overall, combining positive emotion induction with the CK-SVM classifier proved useful for detecting MDD on the basis of EEG signals. In the future, this approach might be used to develop a brain–computer interface system to assist with the detection of MDD in the clinic. Importantly, such a system could be implemented with a low-density electrode montage (seven electrodes), highlighting its practical utility.

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Mobile Virtual Reality as an Educational Platform: A Pilot Study on the Impact of Immersion and Positive Emotion Induction in the Learning Process

Eurasia Journal of Mathematics Science and Technology Education ◽

10.29333/ejmste/85874 ◽

2018 ◽

Vol 14 (6) ◽

Cited By ~ 9

Author(s):

Manuel Contero ◽

Elena Olmos-Raya ◽

Janaina Ferreira-Cavalcanti ◽

M. Concepción Castellanos ◽

Irene Alice Chicchi Giglioli ◽

...

Keyword(s):

Virtual Reality ◽

Pilot Study ◽

Learning Process ◽

Positive Emotion ◽

Emotion Induction ◽

The Impact

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Positive Emotion Induction for Pain Associated with Rheumatoid Arthritis- A Quasi-experimental Study

JOURNAL FOR CLINICAL AND DIAGNOSTIC RESEARCH ◽

10.7860/jcdr/2021/48914.15208 ◽

2021 ◽

Author(s):

Nidhal A Amanullah ◽

Dushad Ram ◽

Subramanian Ramaswami ◽

Muath Alammar

Keyword(s):

Rheumatoid Arthritis ◽

Positive Emotion ◽

Nuclear Family ◽

Psychological Intervention ◽

Substantial Proportion ◽

Severe Illness ◽

Hamilton Depression Scale ◽

Emotion Induction ◽

Significant Difference ◽

Post Test

Introduction: Rheumatoid Arthritis (RA) is a chronic disease and a substantial proportion of patients continue to suffer from chronic pain and disability despite standard pharmacotherapy. A substantial proportion of patients with RA also develop anxiety and depressive symptoms. Positive Emotion Induction (PEI) has been shown to reduce pain symptoms. Aim: To know the effect of positive emotion induction as an adjunctive intervention on RA associated pain and disability. Materials and Methods: The longitudinal hospital based study was conducted at the Outpatient Department of Rheumatology and Psychiatry, Jagadguru Sri Shivarathreeshwara Hospital, Mysuru, Karnataka, India, from January 2018 to June 2019, included 85 consecutive participants with RA were recruited and assessed at baseline with Health Assessment Questionnaire scale {HAQ-DI and Visual Analog Scale (VAS)}, Hamilton Anxiety Scale (HAM-A), and Hamilton Depression Scale (HAM-D). Six session of PEI was done using recreating pleasant memory and the same was practiced at home daily by the patient. All participants were reassessed with the same parameter after three months. Paired sample t-test was done to know the change in the score pre and post test on the score of HAQ-DI and VAS, HAM-A, HAM-D. The value of statistical significance was p-value ≤0.05. Results: The majority of the participants belonged to 40-50 years of age, were married, females, studied to middle school, of low socio-economic status, had a nuclear family. The majority had duration of RA been two to four years, with severe illness and were on regular medication. Statistically significant difference was observed in pre and post test on the score of HAQ-VAS (t=8.23, p<0.05), HAM-A (t=11.40, p<0.05) and HAM-D (t=10.95, p<0.05). Conclusion: Brief psychological intervention (PEI) may be a useful adjunct intervention in patients with RA. Further study is needed to explore the clinical use of the PEI for intervention in RA.

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Effect of Emotions in a Lexical Decision Task

Swiss Journal of Psychology ◽

10.1024/1421-0185/a000193 ◽

2017 ◽

Vol 76 (2) ◽

pp. 71-79 ◽

Cited By ~ 1

Author(s):

Hélène Maire ◽

Renaud Brochard ◽

Jean-Luc Kop ◽

Vivien Dioux ◽

Daniel Zagar

Keyword(s):

Lexical Decision ◽

Lexical Decision Task ◽

Emotional State ◽

Control Group ◽

Decision Task ◽

Attentional Orienting ◽

Emotional States ◽

Sequence Effect ◽

Emotion Induction ◽

Before And After

Abstract. This study measured the effect of emotional states on lexical decision task performance and investigated which underlying components (physiological, attentional orienting, executive, lexical, and/or strategic) are affected. We did this by assessing participants’ performance on a lexical decision task, which they completed before and after an emotional state induction task. The sequence effect, usually produced when participants repeat a task, was significantly smaller in participants who had received one of the three emotion inductions (happiness, sadness, embarrassment) than in control group participants (neutral induction). Using the diffusion model ( Ratcliff, 1978 ) to resolve the data into meaningful parameters that correspond to specific psychological components, we found that emotion induction only modulated the parameter reflecting the physiological and/or attentional orienting components, whereas the executive, lexical, and strategic components were not altered. These results suggest that emotional states have an impact on the low-level mechanisms underlying mental chronometric tasks.

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