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Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Open Access Research Article

Validation of video stimuli for basic emotion elicitation : A multimodal approach using self-reports and physiological signals

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pp. 2465–2489Vol. 47Issue 6June 2026DOI: 10.47974/JIOS-2226XML
Received:
01 Oct 2025
Published Online:
18 May 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2226
Pages:
2465–2489

Abstract

Dependable emotion generation is an essential requirement for enhancing affective computing studies, especially for the development and assessment of strong machine learning models. This study tackles the practical issue of finding suitable, already- existing video stimuli that reliably trigger particular emotional responses. Our main aim was to confirm a selected collection of video clips that can evoke the six fundamental emotions (happiness, sadness, anger, fear, disgust, surprise) along with a neutral condition. In this research, 24 individuals from Vadodara, Gujarat, India, watched the chosen video clips. Emotional reactions were assessed via self-report ratings on specific emotions and a 5-point scale for valence-arousal, and confirmed by physiological indicators (GSR and PPG). The findings reveal an overall emotion elicitation accuracy of 77.38% & 69.64% according to self-reports, and a Cohen’s Kappa value of 0.736 & 0.645 respectively for Session-1 & Session-2, signifying significant alignment between intended and perceived emotions. Valence-arousal mapping validated anticipated emotional distributions. Additionally, substantial differences (p<0.001) were noted in physiological indicators (SCR Peaks, Heart Rate Range, Heart Rate Jumps) among the triggered emotions, offering objective validation. This study recognizes and confirms a collection of impactful video stimuli for inducing emotions, providing a readily accessible resource for the affective computing field without requiring the creation of new datasets, thus simplifying future experimental setups and improving the consistency of emotion-related data gathering.

Keywords

Subject Classifications

68T0168U3592C55

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