PhyMER: Physiological Dataset for Multimodal Emotion Recognition With Personality as a Context

Physiological signals are widely used in the recognition of affective status. Recording of such physiological signals involves elicitation of emotions through different stimuli including video-based stimulus. Considering that the same stimulus videos often induce different emotions in different indi...

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Bibliographic Details
Published in:IEEE access 2023, Vol.11, p.107638-107656
Main Authors: Pant, Sudarshan, Yang, Hyung-Jeong, Lim, Eunchae, Kim, Soo-Hyung, Yoo, Seok-Bong
Format: Article
Language:English
Subjects:
Annotations
Arousal
Blood volume
Context
Data recording
Datasets
EEG
Electrocardiography
Electroencephalography
emotion classification
Emotion recognition
Emotions
Labels
Motion pictures
Personality
personality traits
Physiological signals
Physiology
Skin temperature
Videos
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Summary:Physiological signals are widely used in the recognition of affective status. Recording of such physiological signals involves elicitation of emotions through different stimuli including video-based stimulus. Considering that the same stimulus videos often induce different emotions in different individuals, emotion recognition in such a scenario requires consideration of the individual differences in the consumption of the stimulus content. With this as our goal, we present a Physiological dataset for Multimodal Emotion Recognition (PhyMER) for studying emotion through physiological response with personality as a context. The PhyMER dataset consists of electroencephalogram (EEG), electrodermal activity (EDA), blood volume pulse (BVP), and skin temperature along with the personality traits of 30 participants. We collected the video-based stimulus dataset for emotion elicitation and developed a web-based annotation tool for labeling felt emotions. We compared the stimulus labels and the self-annotation of felt emotions labeled during physiological data recording. Correlation among personalities was analyzed to study the impact of personality on the intensity of emotions in arousal and valence dimensions. Finally, we proposed a baseline model for the classification of emotions using physiological signals. The dataset is publicly available to the academic community for analysis of affective states and the development of emotion recognition models.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3320053