scholarly journals Emotion Recognition and Regulation Based on Stacked Sparse Auto-Encoder Network and Personalized Reconfigurable Music

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 593
Author(s):  
Yinsheng Li ◽  
Wei Zheng
Keyword(s):  
Emotional Regulation ◽  
Emotional State ◽  
Emotional Adjustment ◽  
Recognition Rate ◽  
Traditional Music ◽  
Music Preference ◽  
Emotional States ◽  
Eeg Signal ◽  
Long Duration ◽  
The Brain

Music can regulate and improve the emotions of the brain. Traditional emotional regulation approaches often adopt complete music. As is well-known, complete music may vary in pitch, volume, and other ups and downs. An individual’s emotions may also adopt multiple states, and music preference varies from person to person. Therefore, traditional music regulation methods have problems, such as long duration, variable emotional states, and poor adaptability. In view of these problems, we use different music processing methods and stacked sparse auto-encoder neural networks to identify and regulate the emotional state of the brain in this paper. We construct a multi-channel EEG sensor network, divide brainwave signals and the corresponding music separately, and build a personalized reconfigurable music-EEG library. The 17 features in the EEG signal are extracted as joint features, and the stacked sparse auto-encoder neural network is used to classify the emotions, in order to establish a music emotion evaluation index. According to the goal of emotional regulation, music fragments are selected from the personalized reconfigurable music-EEG library, then reconstructed and combined for emotional adjustment. The results show that, compared with complete music, the reconfigurable combined music was less time-consuming for emotional regulation (76.29% less), and the number of irrelevant emotional states was reduced by 69.92%. In terms of adaptability to different participants, the reconfigurable music improved the recognition rate of emotional states by 31.32%.

scholarly journals EEG-based human emotion recognition using entropy as a feature extraction measure

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Pragati Patel ◽  
Raghunandan R ◽  
Ramesh Naidu Annavarapu
Keyword(s):  
Emotion Recognition ◽  
Emotional State ◽  
Computer Interface ◽  
Eeg Signal ◽  
Emotion Classification ◽  
Emotion Identification ◽  
Physiological Signal ◽  
Wide Range ◽  
Electrical Activities ◽  
The Brain

AbstractMany studies on brain–computer interface (BCI) have sought to understand the emotional state of the user to provide a reliable link between humans and machines. Advanced neuroimaging methods like electroencephalography (EEG) have enabled us to replicate and understand a wide range of human emotions more precisely. This physiological signal, i.e., EEG-based method is in stark comparison to traditional non-physiological signal-based methods and has been shown to perform better. EEG closely measures the electrical activities of the brain (a nonlinear system) and hence entropy proves to be an efficient feature in extracting meaningful information from raw brain waves. This review aims to give a brief summary of various entropy-based methods used for emotion classification hence providing insights into EEG-based emotion recognition. This study also reviews the current and future trends and discusses how emotion identification using entropy as a measure to extract features, can accomplish enhanced identification when using EEG signal.

Relationship between Emotional State and Performance during International Field Hockey Matches

2000 ◽  
Vol 90 (2) ◽  
pp. 691-701 ◽  
Author(s):  
Marc V. Jones ◽  
Roger D. Mace ◽  
Simon Williams
Keyword(s):  
Logistic Regression ◽  
Emotional State ◽  
Emotional States ◽  
Field Hockey ◽  
Long Duration ◽  
Hockey Players ◽  
Competition Period ◽  
And Performance ◽  
The Relationship ◽  
Better Than

The present study examined the relationship between the emotions experienced by 15 international hockey players, both immediately before and during competition, and their performance levels. Data were collected on the players' emotional states using a revised version of the Feelings Scale of Butler, which was completed retrospectively after the match was played. Players reported more annoyance and less tension during the match than before. A logistic regression correctly classified 70.2% of players from the emotional ratings immediately before the match and 85.1% of the players from the ratings during the match as either a good or poor performer. Those individuals who performed well retrospectively reported feeling Nervous and ‘Quick/Alert/Active’ before the game and Confident and Relaxed during the game. The results indicate that emotions fluctuate over the competition period, and in long duration sports assessment of emotion during competition predicts variation in performance better than assessment prior to competition.

scholarly journals Cognitive and affective neuroscience of chronic pain: relevance for Physiotherapy

2018 ◽  
Vol 8 (1) ◽  
pp. 131-137
Author(s):  
Pedro Montoya
Keyword(s):  
Chronic Pain ◽  
Emotional Regulation ◽  
Behavioral Therapy ◽  
Emotional States ◽  
Affective Neuroscience ◽  
Literature Study ◽  
Regulatory Processes ◽  
Current Therapies ◽  
World Center ◽  
The Brain

Background: Chronic pain is the main reason for medical consultation, as well as one of the main burdens of the health system in the developed world. However, current therapies are still inadequate for certain types of chronic pain, as in the case of fibromyalgia syndrome, or cause intolerable side effects (such as opioids). Understanding the neurophysiological and psychobiological bases of chronic pain is crucial to develop adequate and efficient strategies for the multidisciplinary evaluation and treatment of pain. Objective: The aim of this work is to provide a brief summary of the current state of the art to clarify the most effective strategies for the treatment of chronic pain. Methods: Narrative literature study developed in a reference world center to study of chronic pain. Results: In the last decades, it has been demonstrated that the plastic changes that occur in the brain are key for understanding the maintenance of pain over time. Research has provided evidence that patients with chronic pain displayed abnormal brain processing of body information and that negative emotional states can significantly alter brain functioning and amplify the suffering associated with pain. On the other hand, it has been suggested that strengthening emotional regulation skills through cognitive reassessment and suppression as used in cognitive-behavioral therapy or mindfulness can help regulate pain and emotion in patients with chronic pain. However, the brain mechanisms involved in these regulatory processes must still be elucidated, before being transferred to clinical practice. Conclusion: Cognitive and affective neuroscience is fundamental to physiotherapists understanding chronic pain.

scholarly journals Machine Learning and EEG for Emotional State Estimation

2021 ◽  
Author(s):  
Krzysztof Kotowski ◽  
Katarzyna Stapor
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
State Estimation ◽  
Emotional State ◽  
Emotional States ◽  
Eeg Signal ◽  
Self Assessment ◽  
Emotional Perception ◽  
Automatic Systems ◽  
Emotion Models

Defining “emotion” and its accurate measuring is a notorious problem in the psychology domain. It is usually addressed with subjective self-assessment forms filled manually by participants. Machine learning methods and EEG correlates of emotions enable to construction of automatic systems for objective emotion recognition. Such systems could help to assess emotional states and could be used to improve emotional perception. In this chapter, we present a computer system that can automatically recognize an emotional state of a human, based on EEG signals induced by a standardized affective picture database. Based on the EEG signal, trained deep neural networks are then used together with mappings between emotion models to predict the emotions perceived by the participant. This, in turn, can be used for example in validation of affective picture databases standardization.

scholarly journals Cognitive Emotional Regulation Model in Human-Robot Interaction

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Xin Liu ◽  
Lun Xie ◽  
Anqi Liu ◽  
Dan Li
Keyword(s):  
Facial Expressions ◽  
Emotional Regulation ◽  
Emotional State ◽  
Human Robot Interaction ◽  
Emotional States ◽  
External Energy ◽  
Wide Range ◽  
Emotional Model ◽  
Regulation Model ◽  
Emotional Transition

This paper integrated Gross cognitive process into the HMM (hidden Markov model) emotional regulation method and implemented human-robot emotional interaction with facial expressions and behaviors. Here, energy was the psychological driving force of emotional transition in the cognitive emotional model. The input facial expression was translated into external energy by expression-emotion mapping. Robot’s next emotional state was determined by the cognitive energy (the stimulus after cognition) and its own current emotional energy’s size and source’s position. The two random quantities in emotional transition process—the emotional family and the specific emotional state in the AVS (arousal-valence-stance) 3D space—were used to simulate human emotion selection. The model had been verified by an emotional robot with 10 degrees of freedom and more than 100 kinds of facial expressions. Experimental results show that the emotional regulation model does not simply provide the typical classification and jump in terms of a set of emotional labels but that it operates in a 3D emotional space enabling a wide range of intermediary emotional states to be obtained. So the robot with cognitive emotional regulation model is more intelligent and real; moreover it can give full play to its emotional diversification in the interaction.

Effect of Emotions in a Lexical Decision Task

2017 ◽  
Vol 76 (2) ◽  
pp. 71-79 ◽  
Author(s):  
Hélène Maire ◽  
Renaud Brochard ◽  
Jean-Luc Kop ◽  
Vivien Dioux ◽  
Daniel Zagar
Keyword(s):  
Lexical Decision ◽  
Lexical Decision Task ◽  
Emotional State ◽  
Control Group ◽  
Decision Task ◽  
Attentional Orienting ◽  
Emotional States ◽  
Sequence Effect ◽  
Emotion Induction ◽  
Before And After

Abstract. This study measured the effect of emotional states on lexical decision task performance and investigated which underlying components (physiological, attentional orienting, executive, lexical, and/or strategic) are affected. We did this by assessing participants’ performance on a lexical decision task, which they completed before and after an emotional state induction task. The sequence effect, usually produced when participants repeat a task, was significantly smaller in participants who had received one of the three emotion inductions (happiness, sadness, embarrassment) than in control group participants (neutral induction). Using the diffusion model ( Ratcliff, 1978 ) to resolve the data into meaningful parameters that correspond to specific psychological components, we found that emotion induction only modulated the parameter reflecting the physiological and/or attentional orienting components, whereas the executive, lexical, and strategic components were not altered. These results suggest that emotional states have an impact on the low-level mechanisms underlying mental chronometric tasks.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

Relation Between Level of Prefrontal Activity and Subject's Performance

1999 ◽  
Vol 13 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Laurence Casini ◽  
Françoise Macar ◽  
Marie-Hélène Giard
Keyword(s):  
Brain Activity ◽  
Sensory Information ◽  
Practice Phase ◽  
Trained Subjects ◽  
Anagram Task ◽  
Economic Information ◽  
Long Duration ◽  
Level Of Activity ◽  
The Brain ◽  
Processing Mechanism

Abstract The experiment reported here was aimed at determining whether the level of brain activity can be related to performance in trained subjects. Two tasks were compared: a temporal and a linguistic task. An array of four letters appeared on a screen. In the temporal task, subjects had to decide whether the letters remained on the screen for a short or a long duration as learned in a practice phase. In the linguistic task, they had to determine whether the four letters could form a word or not (anagram task). These tasks allowed us to compare the level of brain activity obtained in correct and incorrect responses. The current density measures recorded over prefrontal areas showed a relationship between the performance and the level of activity in the temporal task only. The level of activity obtained with correct responses was lower than that obtained with incorrect responses. This suggests that a good temporal performance could be the result of an efficacious, but economic, information-processing mechanism in the brain. In addition, the absence of this relation in the anagram task results in the question of whether this relation is specific to the processing of sensory information only.

scholarly journals Dogs can infer implicit information from human emotional expressions

2021 ◽  
Author(s):  
Natalia Albuquerque ◽  
Daniel S. Mills ◽  
Kun Guo ◽  
Anna Wilkinson ◽  
Briseida Resende
Keyword(s):  
Emotional State ◽  
Food Resource ◽  
Emotional Expressions ◽  
Emotional States ◽  
Emotional Information ◽  
Implicit Information ◽  
Emotional Display ◽  
Affective Information ◽  
Social Decision Making ◽  
Current Behaviour

AbstractThe ability to infer emotional states and their wider consequences requires the establishment of relationships between the emotional display and subsequent actions. These abilities, together with the use of emotional information from others in social decision making, are cognitively demanding and require inferential skills that extend beyond the immediate perception of the current behaviour of another individual. They may include predictions of the significance of the emotional states being expressed. These abilities were previously believed to be exclusive to primates. In this study, we presented adult domestic dogs with a social interaction between two unfamiliar people, which could be positive, negative or neutral. After passively witnessing the actors engaging silently with each other and with the environment, dogs were given the opportunity to approach a food resource that varied in accessibility. We found that the available emotional information was more relevant than the motivation of the actors (i.e. giving something or receiving something) in predicting the dogs’ responses. Thus, dogs were able to access implicit information from the actors’ emotional states and appropriately use the affective information to make context-dependent decisions. The findings demonstrate that a non-human animal can actively acquire information from emotional expressions, infer some form of emotional state and use this functionally to make decisions.

An experimental study on the effect of emotion lines in comics

Semiotica ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Amitash Ojha ◽  
Charles Forceville ◽  
Bipin Indurkhya
Keyword(s):  
Experimental Study ◽  
Emotional State ◽  
Negative Emotions ◽  
Emotional States ◽  
One To One

Abstract Both mainstream and art comics often use various flourishes surrounding characters’ heads. These so-called “pictorial runes” (also called “emanata”) help convey the emotional states of the characters. In this paper, using (manipulated) panels from Western and Indian comic albums as well as neutral emoticons and basic shapes in different colors, we focus on the following two issues: (a) whether runes increase the awareness in comics readers about the emotional state of the character; and (b) whether a correspondence can be found between the types of runes (twirls, spirals, droplets, and spikes) and specific emotions. Our results show that runes help communicate emotion. Although no one-to-one correspondence was found between the tested runes and specific emotions, it was found that droplets and spikes indicate generic emotions, spirals indicate negative emotions, and twirls indicate confusion and dizziness.

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