Quantification of anticipation of excitement with three-axial model of emotion with EEG

Abstract

Objectives. Multiple facets of human emotions underlie diverse and sparse neural mechanisms. Amongst many models of emotions, the circumplex model of emotion is one of a significant theory. The use of the circumplex model allows us to model variable aspects of emotion; however, such momentary expression of one's internal mental state still lacks to consider another, the third dimension of time. Here, we report an exploratory attempt to build a three-axial model of human emotion to model our sense of anticipatory excitement, "Waku-Waku (in Japanese)," when people are predictively coding upcoming emotional events.Approach. Electroencephalography (EEG) was recorded from 28 young adult participants while they mentalized upcoming emotional pictures. Three auditory tones were used as indicative cues, predicting the likelihood of valence of an upcoming picture, either positive, negative, or unknown. While seeing an image, participants judged its emotional valence during the task, and subsequently rated their subjective experiences on valence, arousal, expectation, and Waku-Waku immediately after the experiment. The collected EEG data were then analyzed to identify contributory neural signatures for each of the three axes.Main Results. A three axial model was built to quantify Waku-Waku. As was expected, this model revealed considerable contribution of the third dimension over the classical twodimension model. Distinctive EEG components were identified. Furthermore, a novel brainemotion interface is proposed and validated within the scope of limitations.Significance. The proposed notion may shed new light on the theories of emotion and supports multiplex dimensions of emotion. With an introduction of the cognitive domain for a braincomputer-interface, we propose a novel brain-emotion-interface. Limitations and potential applications are discussed.

Brain–heart dynamics during emotional processing under uncertain conditions: An index of depression risk

“…Finally, participants rated their emotions after seeing the images. For further details, please refer to Machizawa et al (2020).…”

“…Twenty-six healthy participants (16 females; mean and standard deviation ( SD ) of age: 22.19 ± 1.86 years old) with no history of psychiatric, psychological, or cardiac disorders, participated in concurrent EEG-ECG recording during an emotion-evoking picture-evaluation task. Data were acquired from a previous study (Machizawa et al, 2020).…”

Emotion dynamics as hierarchical Bayesian inference in time

“…The two underlying dimensions -valence and arousal -consistently identified to describe emotional experiences, are effective in defining disconnected feelings 7 but have been shown to be inefficient in characterising complex real-world emotions 8,9 . To incorporate this complexity, varying combinations of context-driven dimensions like impulsivity, frustration, excitement have been used, which limits studies to describe occurrence of specific emotional states [10][11][12] . Hence, these studies are not well suited to capture the fundamental properties of emotion.…”

Emotion Dynamics as Hierarchical Bayesian Inference in Time

“…The two underlying dimensions -valence and arousal -consistently identified to describe emotional experiences, are effective in defining disconnected feelings 7 but have been shown to be inefficient in characterising complex real-world emotions 8,9 . To incorporate this complexity, varying combinations of context-driven dimensions like impulsivity, frustration, excitement have been used, which limits studies to describe occurrence of specific emotional states [10][11][12] . Hence, these studies are not well suited to capture the fundamental properties of emotion.…”