Mi-Sook Park

Analysis of physiological signals for recognition of boredom, pain, and surprise emotions

BackgroundThe aim of the study was to examine the differences of boredom, pain, and surprise. In addition to that, it was conducted to propose approaches for emotion recognition based on physiological signals.MethodsThree emotions, boredom, pain, and surprise, are induced through the presentation of emotional stimuli and electrocardiography (ECG), electrodermal activity (EDA), skin temperature (SKT), and photoplethysmography (PPG) as physiological signals are measured to collect a dataset from 217 participants when experiencing the emotions. Twenty-seven physiological features are extracted from the signals to classify the three emotions. The discriminant function analysis (DFA) as a statistical method, and five machine learning algorithms (linear discriminant analysis (LDA), classification and regression trees (CART), self-organizing map (SOM), Naïve Bayes algorithm, and support vector machine (SVM)) are used for classifying the emotions.ResultsThe result shows that the difference of physiological responses among emotions is significant in heart rate (HR), skin conductance level (SCL), skin conductance response (SCR), mean skin temperature (meanSKT), blood volume pulse (BVP), and pulse transit time (PTT), and the highest recognition accuracy of 84.7 % is obtained by using DFA.ConclusionsThis study demonstrates the differences of boredom, pain, and surprise and the best emotion recognizer for the classification of the three emotions by using physiological signals.

Brain functions associated with verbal working memory tasks among young males with alcohol use disorders.

This study aimed to investigate the differences in brain functions during verbal working memory between individuals with alcohol use disorders (AUD) and normal controls. fMRI was used to scan brain activations associated with verbal working memory while participants performed 2-back and 0-back tasks. A total of 21 young male college students participated in the study. Eleven of those who clinically met the criteria for AUD were assigned to the AUD group, whereas ten demographically similar subjects who were social drinkers but not AUD were assigned to the normal control group. The AUD group showed less activation in bilateral frontal and precentral, left superior temporal, left superior parietal, and left cerebellar cortex during the 2-back task relative to 0-back task compared to the normal control group. In contrast, the control group showed less activation only in the right uncus than the AUD group. These results suggest that subjects with AUD present abnormality in brain functioning during verbal working memory.

Degree of extraversion and physiological responses to physical pain and sadness.

Extraversion is a personality frequently discussed as one of the strongest and most consistent factors that relates to individual subjective wellbeing. The goal of this study was to better understand how people with varying degrees of extraversion psychologically and physiologically respond differently to unpleasant circumstances. Emotional responses (e.g., levels of intensity, valence, and arousal) were assessed in determining the sensitivity level to negative stimuli that were specifically designed to provoke physical pain and sadness emotion. Physiological changes (e.g., heart rate (HR), blood volume pulse (BVP), and respiratory sinus arrhythmia (RSA)) were also measured during pain and sadness to observe sympathetic and parasympathetic activities. Our results showed that the degree of extraversion was associated with less unpleasant responses to sadness, less HR responses to both pain and sadness, and greater RSA responses to sadness. The findings suggest that the lower HR reactivity to painful and sad situations and greater RSA reactivity to sad situations in extraversion could be possibly due to increased parasympathetic activity. Additionally, enhanced parasympathetic activity to negative situations may explain an important mechanism underlying the positive connection between extraversion and subjective wellbeing.

P300 speller using a new stimulus presentation paradigm

In most implementation of a P300 BCI, RCP was used. However, RCP is susceptible to adjacency-distraction errors. This study presents a novel P300-based BCI stimulus presentation-the sub-block paradigm (SBP). This alternative paradigm substantially improved P300 speller performance by using adjacency-distraction effect. In this study, the SBP performance was compared to that of the standard RCP. Consequently, the SBP was more correct in P300 speller performance as well as more convenient for users than the RCP.

Neural substrates involved in anger induced by audio-visual film clips among patients with alcohol dependency

BackgroundVery little is known about the neural circuitry underlying anger processing among alcoholics. The purpose of this study was to examine the altered brain activity of alcoholic individuals during transient anger emotion.MethodsUsing functional magnetic resonance imaging (fMRI), 18 male patients diagnosed with alcohol dependence in an inpatient alcohol treatment facility and 16 social drinkers with similar demographics were scanned during the viewing of anger-provoking film clips.ResultsWhile there was no significant difference in the level of experienced anger between alcohol-dependent patients and non-alcoholic controls, significantly greater activation was observed in the bilateral dorsal anterior cingulate cortex (dACC) and the right precentral gyrus among alcoholic patients compared to the normal controls.ConclusionsIn summary, specific brain regions were identified that are associated with anger among patients with alcohol dependency.

Eeg-based emotion recogntion during emotionally evocative films

It is difficult to classify anger, fear, and surprise emotions with autonomic nervous system response patterns, because these three emotions show similar levels of valence and arousal dimensions. The purpose of this study was to classify three emotions by using EEG signals. Linear discriminant analysis (LDA) using three types of EEG characteristics showed that the mean recognition accuracy was 66.3%. These findings reveal that three emotions were successfully able to be classified based on EEG signals.