A Method for Quantifying the Emotional Intensity and Duration of a Startle Reaction with Customized Fractal Dimensions of EEG Signals

The assessment of emotions with fractal dimensions of EEG signals has been attempted before, but the quantification of the intensity and duration of sudden and short emotions remains a challenge. This paper suggests a method for this purpose, by using a new fractal dimension algorithm and by adjusting the amplitude of the EEG signal in order to obtain maximal separation of high and low fractal dimensions. The emotion was induced by embedding a scary image at 20 seconds in landscape videos of 60 seconds length. The new method did not only detect the onset of the emotion correctly, but also revealed its duration and intensity. The intensity is based on the magnitude and impulse of the fractal dimension signal. It is also shown that Higuchi’s method does not always detect emotion spikes correctly;on the contrary, the region of the expected emotional response can be represented by fractal dimensions smaller than the rest of the signal, whereas the new method directly reveals distinct spikes. The duration of these spikes was 10 - 11 seconds. The magnitude of these spikes varied across the EEG channels. The build-up and cool-down of the emotions can occur with steep and flat gradients.

Predicting Complex Word Emotions and Topics through a Hierarchical Bayesian Network

In this paper, we provide a Word Emotion Topic （WET） model to predict the complex word e- motion information from text, and discover the dis- trbution of emotions among different topics. A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories： joy, love, expectation, sur- prise, anxiety, sorrow, anger and hate. We use a hi- erarchical Bayesian network to model the emotions and topics in the text. Both the complex emotions and topics are drawn from raw texts, without con- sidering any complicated language features. Our ex- periment shows promising results of word emotion prediction, which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields（CRFs） on raw text. We also explore the topic distribution by examining the emotion topic variation in an emotion topic diagram.

Implicit,But Not Explicit,Emotion Regulation Relieves Unpleasant Neural Responses Evoked by High-Intensity Negative Images

Evidence suggests that explicit reappraisal has limited regulatory effects on high-intensity emotions,mainly due to the depletion of cognitive resources occupied by the high-intensity emotional stimulus itself.The implicit form of reappraisal has proved to be resource-saving and therefore might be an ideal strategy to achieve the desired regulatory effect in high-intensity situations.In this study,we explored the regulatory effect of explicit and implicit reappraisal when participants encountered low-and high-intensity negative images.The subjective emotional rating indicated that both explicit and implicit reappraisal down-regulated negative experiences,irrespective of intensity.However,the amplitude of the parietal late positive potential(LPP;a neural index of experienced emotional intensity)showed that only implicit reappraisal had significant regulatory effects in the high-intensity context,though both explicit and implicit reappraisal successfully reduced the emotional neural responses elicited by low-intensity negative images.Meanwhile,implicit reappraisal led to a smaller frontal LPP amplitude(an index of cognitive cost)compared to explicit reappraisal,indicating that the implementation of implicit reappraisal consumes limited cognitive control resources.Furthermore,we found a prolonged effect of implicit emotion regulation introduced by training procedures.Taken together,these findings not only reveal that implicit reappraisal is suitable to relieve high-intensity negative experiences as well as neural responses,but also highlight the potential benefit of trained implicit regulation in clinical populations whose frontal control resources are limited.

Cognitive-affective regulation process for micro-expressions based on Gaussian cloud distribution

In this paper, we explore the process of emotional state transition. And the process is impacted by emotional state of interaction objects. First of all, the cognitive reasoning process and the micro-expressions recognition is the basis of affective computing adjustment process. Secondly, the threshold function and attenuation function are proposed to quantify the emotional changes. In the actual environment, the emotional state of the robot and external stimulus are also quantified as the transferring probability. Finally, the Gaussian cloud distribution is introduced to the Gross model to calculate the emotional transitional probabilities. The experimental results show that the model in human-computer interaction can effectively regulate the emotional states, and can significantly improve the humanoid and intelligent ability of the robot. This model is consistent with experimental and emulational significance of the psychology, and allows the robot to get rid of the mechanical emotional transfer process.