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Transition of emotions from the negatively excited state to positive unexcited state: an ERP perspective

Singh Moon Inder, Singh Mandeep

Metrology and Measurement Systems

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2022

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Vol. 29, nr 2

405--423

EN

The cognitive aspects like perception, problem-solving, thinking, task performance, etc., are immensely influenced by emotions making it necessary to study emotions. The best state of emotion is the positive unexcited state, also known as the HighValence LowArousal (HVLA) state of the emotion. The psychologists endeavour to bring the subjects from a negatively excited state of emotion (Low Valence High Arousal state) to a positive unexcited state of emotion (High Valence Low Arousal state). In the first part of this study, a four-class subject independent emotion classifier was developed with an SVM polynomial classifier using average Event Related Potential (ERP) and differential average ERP attributes. The visually evoked Electroencephalogram (EEG) signals were acquired from 24 subjects. The four-class classification accuracy was 83% using average ERP attributes and 77% using differential average ERP attributes. In the second part of the study, the meditative intervention was applied to 20 subjects who declared themselves negatively excited (in Low Valence High Arousal state of emotion). The EEG signals were acquired before and after the meditative intervention. The four-class subject independent emotion classifier developed in Study 1 correctly classified these 20 subjects to be in a negatively excited state of emotion. After the intervention, 16 subjects self-assessed themselves to be in a positive unexcited (HVLA) state of emotion (which shows the intervention accuracy of 80%). Testing a four-class subject independent emotion classifier on the EEG data acquired after the meditative intervention validated 13 of 16 subjects in a positive unexcited state, yielding an accuracy of 81.3%.

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Electrodermal activity measurements for detection of emotional arousal

Kołodziej M., Tarnowski P., Majkowski A., Rak R. J.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2019

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Vol. 67, nr 4

813--826

EN

In this article, we present a comprehensive measurement system to determine the level of user emotional arousal by the analysis of electrodermal activity (EDA). A number of EDA measurements were collected, while emotions were elicited using specially selected movie sequences. Data collected from 16 participants of the experiment, in conjunction with those from personal questionnaires, were used to determine a large number of 20 features of the EDA, to assess the emotional state of a user. Feature selection was performed using signal processing and analysis methods, while considering user declarations. The suitability of the designed system for detecting the level of emotional arousal was fully confirmed, throughout the number of experiments. The average classification accuracy for two classes of the least and the most stimulating movies varies within the range of 61‒72%.

3

Modeling the 2D space of emotions based on the poincare plot of heart rate variability signal

Moharreri S., Dabanloo N. J., Maghooli K.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 4

794--809

EN

Emotions mean accepting, understanding, and recognizing something with one's senses. The physiological signals generated from the internal organs of the body can objectively and realistically reflect changes in real-time human emotions and monitor the state of the body. In this study, the two-dimensional space-based emotion model was introduced on the basis of Poincare's two-dimensional plot of the signal of heart rate variability. Four main colors of psychology, blue, red, green, and yellow were used as a stimulant of emotion, and the ECG signals from 70 female students were recorded. Using extracted features of Poincare plot and heart rate asymmetry, two tree based models estimated the levels of arousal and valence with 0.05 mean square errors, determined an appropriate estimation of these two parameters of emotion. In the next stage of the study, four different emotions mean pleasure, anger, joy, and sadness, were classified using IF-THEN rules with the accuracy of 95.71%. The results show the color red is associated with more excitement and anger, while green has small anxiety. So, this system provides a measure for numerical comparison of mental states and makes it possible to model emotions for interacting with the computer and control mental states independently of the pharmaceutical methods.