Effective affective EEG-based indicators in emotion-evoking VR environments: an evidence from machine learning

被引：7
作者：
Castiblanco Jimenez, Ivonne Angelica [1 ]
Olivetti, Elena Carlotta [1 ]
Vezzetti, Enrico [1 ]
Moos, Sandro [1 ]
Celeghin, Alessia [2 ]
Marcolin, Federica [1 ]
机构：
[1] Department of Management and Production Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, Turin
[2] Department of Psychology, Università degli Studi di Torino, Via Verdi, 8, Turin
来源：
Neural Computing and Applications | 2024年 / 36卷 / 35期
关键词：
Affective elicitation; EEG; Emotion assessment; VR;
D O I：
10.1007/s00521-024-10240-z
中图分类号：
学科分类号：
摘要：
This study investigates the use of electroencephalography (EEG) to characterize emotions and provides insights into the consistency between self-reported and machine learning outcomes. Thirty participants engaged in five virtual reality environments designed to elicit specific emotions, while their brain activity was recorded. The participants self-assessed their ground truth emotional state in terms of Arousal and Valence through a Self-Assessment Manikin. Gradient Boosted Decision Tree was adopted as a classification algorithm to test the EEG feasibility in the characterization of emotional states. Distinctive patterns of neural activation corresponding to different levels of Valence and Arousal emerged, and a noteworthy correspondence between the outcomes of the self-assessments and the classifier suggested that EEG-based affective indicators can be successfully applied in emotional characterization, shedding light on the possibility of using them as ground truth measurements. These findings provide compelling evidence for the validity of EEG as a tool for emotion characterization and its contribution to a better understanding of emotional activation. © The Author(s) 2024.
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页码：22245 / 22263
页数：18
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