An exploratory study of the effect of age and gender on face scanning during affect recognition in immersive virtual reality

A person with impaired emotion recognition is not able to correctly identify facial expressions represented by other individuals. The aim of the present study is to assess eyes gaze and facial emotion recognition in a healthy population using dynamic avatars in immersive virtual reality (IVR). For the first time, the viewing of each area of interest of the face in IVR is studied by gender and age. This work in healthy people is conducted to assess the future usefulness of IVR in patients with deficits in the recognition of facial expressions. Seventy-four healthy volunteers participated in the study. The materials used were a laptop computer, a game controller, and a head-mounted display. Dynamic virtual faces randomly representing the six basic emotions plus neutral expression were used as stimuli. After the virtual human represented an emotion, a response panel was displayed with the seven possible options. Besides storing the hits and misses, the software program internally divided the faces into different areas of interest (AOIs) and recorded how long participants looked at each AOI. As regards the overall accuracy of the participants' responses, hits decreased from the youngest to the middle-aged and older adults. Also, all three groups spent the highest percentage of time looking at the eyes, but younger adults had the highest percentage. It is also noteworthy that attention to the face compared to the background decreased with age. Moreover, the hits between women and men were remarkably similar and, in fact, there were no statistically significant differences between them. In general, men paid more attention to the eyes than women, but women paid more attention to the forehead and mouth. In contrast to previous work, our study indicates that there are no differences between men and women in facial emotion recognition. Moreover, in line with previous work, the percentage of face viewing time for younger adults is higher than for older adults. However, contrary to earlier studies, older adults look more at the eyes than at the mouth.Consistent with other studies, the eyes are the AOI with the highest percentage of viewing time. For men the most viewed AOI is the eyes for all emotions in both hits and misses. Women look more at the eyes for all emotions, except for joy, fear, and anger on hits. On misses, they look more into the eyes for almost all emotions except surprise and fear.

A Real-Time Automated Defect Detection System for Ceramic Pieces Manufacturing Process Based on Computer Vision with Deep Learning

Defect detection is a key element of quality control in today's industries, and the process requires the incorporation of automated methods, including image sensors, to detect any potential defects that may occur during the manufacturing process. While there are various methods that can be used for inspecting surfaces, such as those of metal and building materials, there are only a limited number of techniques that are specifically designed to analyze specialized surfaces, such as ceramics, which can potentially reveal distinctive anomalies or characteristics that require a more precise and focused approach. This article describes a study and proposes an extended solution for defect detection on ceramic pieces within an industrial environment, utilizing a computer vision system with deep learning models. The solution includes an image acquisition process and a labeling platform to create training datasets, as well as an image preprocessing technique, to feed a machine learning algorithm based on convolutional neural networks (CNNs) capable of running in real time within a manufacturing environment. The developed solution was implemented and evaluated at a leading Portuguese company that specializes in the manufacturing of tableware and fine stoneware. The collaboration between the research team and the company resulted in the development of an automated and effective system for detecting defects in ceramic pieces, achieving an accuracy of 98.00% and an F1-Score of 97.29%.

Internet-based identification of anxiety in university students using text and facial emotion analysis

Background

Anxiety in university students can lead to poor academic performance and even dropout. The Adult Manifest Anxiety Scale (AMAS-C) is a validated measure designed to assess the level and nature of anxiety in college students.

Objective

The aim of this study is to provide internet-based alternatives to the AMAS-C in the automated identification and prediction of anxiety in young university students. Two anxiety prediction methods, one based on facial emotion recognition and the other on text emotion recognition, are described and validated using the AMAS-C Test Anxiety, Lie and Total Anxiety scales as ground truth data.

Methods

The first method analyses facial expressions, identifying the six basic emotions (anger, disgust, fear, happiness, sadness, surprise) and the neutral expression, while the students complete a technical skills test. The second method examines emotions in posts classified as positive, negative and neutral in the students' profile on the social network Facebook. Both approaches aim to predict the presence of anxiety.

Results

Both methods achieved a high level of precision in predicting anxiety and proved to be effective in identifying anxiety disorders in relation to the AMAS-C validation tool. Text analysis-based prediction showed a slight advantage in terms of precision (86.84 %) in predicting anxiety compared to face analysis-based prediction (84.21 %).

Conclusions

The applications developed can help educators, psychologists or relevant institutions to identify at an early stage those students who are likely to fail academically at university due to an anxiety disorder.

Effect of Action Units, Viewpoint and Immersion on Emotion Recognition Using Dynamic Virtual Faces

Facial affect recognition is a critical skill in human interactions that is often impaired in psychiatric disorders. To address this challenge, tests have been developed to measure and train this skill. Recently, virtual human (VH) and virtual reality (VR) technologies have emerged as novel tools for this purpose. This study investigates the unique contributions of different factors in the communication and perception of emotions conveyed by VHs. Specifically, it examines the effects of the use of action units (AUs) in virtual faces, the positioning of the VH (frontal or mid-profile), and the level of immersion in the VR environment (desktop screen versus immersive VR). Thirty-six healthy subjects participated in each condition. Dynamic virtual faces (DVFs), VHs with facial animations, were used to represent the six basic emotions and the neutral expression. The results highlight the important role of the accurate implementation of AUs in virtual faces for emotion recognition. Furthermore, it is observed that frontal views outperform mid-profile views in both test conditions, while immersive VR shows a slight improvement in emotion recognition. This study provides novel insights into the influence of these factors on emotion perception and advances the understanding and application of these technologies for effective facial emotion recognition training.

Vision-Based Flying Obstacle Detection for Avoiding Midair Collisions: A Systematic Review

This paper presents a systematic review of articles on computer-vision-based flying obstacle detection with a focus on midair collision avoidance. Publications from the beginning until 2022 were searched in Scopus, IEEE, ACM, MDPI, and Web of Science databases. From the initial 647 publications obtained, 85 were finally selected and examined. The results show an increasing interest in this topic, especially in relation to object detection and tracking. Our study hypothesizes that the widespread access to commercial drones, the improvements in single-board computers, and their compatibility with computer vision libraries have contributed to the increase in the number of publications. The review also shows that the proposed algorithms are mainly tested using simulation software and flight simulators, and only 26 papers report testing with physical flying vehicles. This systematic review highlights other gaps to be addressed in future work. Several identified challenges are related to increasing the success rate of threat detection and testing solutions in complex scenarios.

A Systematic Review on Deep Learning with CNNs Applied to Surface Defect Detection

Surface defect detection with machine learning has become an important tool in industries and a large field of study for researchers or workers in recent years. It is necessary to have a simplified source of information that helps us to better focus on one type of surface. In this systematic review, we present a classification for surface defect detection based on convolutional neural networks (CNNs) focused on surface types. Findings: Out of 253 records identified, 59 primary studies were eligible. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we analyzed the structures of each study and the concepts related to defects and their types on surfaces. The presented review is mainly focused on finding a classification for the types of surfaces most used in industry (metal, building, ceramic, wood, and special). We delve into the specifics of each surface category, offering illustrative examples of their applications within both industrial and laboratory settings. Furthermore, we propose a new taxonomy of machine learning based on the obtained results and collected information. We summarized the studies and extracted the main characteristics such as type of surface, problem types, timeline, type of network, techniques, and datasets. Among the most relevant results of our analysis, we found that the metallic surface is the most used, as it is the one found in 62.71% of the studies, and the most prevalent problem type is classification, accounting for 49.15% of the total. Furthermore, we observe that transfer learning was employed in 83.05% of the studies, while data augmentation was utilized in 59.32%. Our findings also provide insights into the cameras most frequently employed, along with the strategies adopted to address illumination challenges present in certain articles and the approach to creating datasets for real-world applications. The main results presented in this review allow for a quick and efficient search of information for researchers and professionals interested in improving the results of their defect detection projects. Finally, we analyzed the trends that could open new fields of study for future research in the area of surface defect detection.

Facial emotion recognition in patients with depression compared to healthy controls when using human avatars

The negative, mood-congruent cognitive bias described in depression, as well as excessive rumination, have been found to interfere with emotional processing. This study focuses on the assessment of facial recognition of emotions in patients with depression through a new set of dynamic virtual faces (DVFs). The sample consisted of 54 stable patients compared to 54 healthy controls. The experiment consisted in an emotion recognition task using non-immersive virtual reality (VR) with DVFs of six basic emotions and neutral expression. Patients with depression showed a worst performance in facial affect recognition compared to healthy controls. Age of onset was negatively correlated with emotion recognition and no correlation was observed for duration of illness or number of lifetime hospitalizations. There was no correlation for the depression group between emotion recognition and degree of psychopathology, excessive rumination, degree of functioning, or quality of life. Hence, it is important to improve and validate VR tools for emotion recognition to achieve greater methodological homogeneity of studies and to be able to establish more conclusive results.

Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review

This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six scientific databases, fifty-nine were finally selected according to various criteria established. The systematic review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, pre-processing, processing and feature extraction. Finally, all ML techniques applied to the features of these signals for arousal classification have been studied. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high-performance values. In contrast, it has been shown that unsupervised learning is not present in the detection of arousal through EDA. This systematic review concludes that the use of EDA for the detection of arousal is widely spread, with particularly good results in classification with the ML methods found.

Behavioral intention of mental health practitioners toward the adoption of virtual humans in affect recognition training

This paper explores the key factors influencing mental health professionals' behavioral intention to adopt virtual humans as a means of affect recognition training. Therapies targeting social cognition deficits are in high demand given that these deficits are related to a loss of functioning and quality of life in several neuropsychiatric conditions such as schizophrenia, autism spectrum disorders, affective disorders, and acquired brain injury. Therefore, developing new therapies would greatly improve the quality of life of this large cohort of patients. A questionnaire based on the second revision of the Unified Theory of Acceptance and Use of Technology (UTAUT2) questionnaire was used for this study. One hundred and twenty-four mental health professionals responded to the questionnaire after viewing a video presentation of the system. The results confirmed that mental health professionals showed a positive intention to use virtual reality tools to train affect recognition, as they allow manipulation of social interaction with patients. Further studies should be conducted with therapists from other countries to reach more conclusions.

Entropy and the Emotional Brain: Overview of a Research Field

During the last years, there has been a notable increase in the number of studies focused on the assessment of brain dynamics for the recognition of emotional states by means of nonlinear methodologies. More precisely, different entropy metrics have been applied for the analysis of electroencephalographic recordings for the detection of emotions. In this sense, regularity-based entropy metrics, symbolic predictability-based entropy indices, and different multiscale and multilag variants of the aforementioned methods have been successfully tested in a series of studies for emotion recognition from the EEG recording. This chapter aims to unify all those contributions to this scientific area, summarizing the main discoverings recently achieved in this research field.

Evaluation of Brain Functional Connectivity from Electroencephalographic Signals Under Different Emotional States

The identification of the emotional states corresponding to the four quadrants of the valence/arousal space has been widely analyzed in the scientific literature by means of multiple techniques. Nevertheless, most of these methods were based on the assessment of each brain region separately, without considering the possible interactions among different areas. In order to study these interconnections, this study computes for the first time the functional connectivity metric called cross-sample entropy for the analysis of the brain synchronization in four groups of emotions from electroencephalographic signals. Outcomes reported a strong synchronization in the interconnections among central, parietal and occipital areas, while the interactions between left frontal and temporal structures with the rest of brain regions presented the lowest coordination. These differences were statistically significant for the four groups of emotions. All emotions were simultaneously classified with a 95.43% of accuracy, overcoming the results reported in previous studies. Moreover, the differences between high and low levels of valence and arousal, taking into account the state of the counterpart dimension, also provided notable findings about the degree of synchronization in the brain within different emotional conditions and the possible implications of these outcomes from a psychophysiological point of view.

Augmented Humanity: A Systematic Mapping Review

Augmented humanity (AH) is a term that has been mentioned in several research papers. However, these papers differ in their definitions of AH. The number of publications dealing with the topic of AH is represented by a growing number of publications that increase over time, being high impact factor scientific contributions. However, this terminology is used without being formally defined. The aim of this paper is to carry out a systematic mapping review of the different existing definitions of AH and its possible application areas. Publications from 2009 to 2020 were searched in Scopus, IEEE and ACM databases, using search terms "augmented human", "human augmentation" and "human 2.0". Of the 16,914 initially obtained publications, a final number of 133 was finally selected. The mapping results show a growing focus on works based on AH, with computer vision being the index term with the highest number of published articles. Other index terms are wearable computing, augmented reality, human-robot interaction, smart devices and mixed reality. In the different domains where AH is present, there are works in computer science, engineering, robotics, automation and control systems and telecommunications. This review demonstrates that it is necessary to formalize the definition of AH and also the areas of work with greater openness to the use of such concept. This is why the following definition is proposed: "Augmented humanity is a human-computer integration technology that proposes to improve capacity and productivity by changing or increasing the normal ranges of human function through the restoration or extension of human physical, intellectual and social capabilities".

Security Mechanisms of a Mobile Health Application for Promoting Physical Activity among Older Adults

Physical activity contributes to the maintenance of health conditions and functioning. However, the percentage of older adults who comply with the recommendations for physical activity levels is low when compared to the same percentages on younger groups. The SmartWalk system aims to encourage older adults to perform physical activity (i.e., walking in the city), which is monitored and adjusted by healthcare providers for best results. The study reported in this article focused on the implementation of SmartWalk security services to keep personal data safe during communications and while at rest, which were validated considering a comprehensive use case. The security framework offers various mechanisms, including an authentication system that was designed to complement the pairs of usernames and passwords with trusted execution environments and token-based features, authorization with different access levels, symmetric and asymmetric key cryptography, critical transactions review, and logging supported by blockchain technology. The resulting implementation contributes for a common understanding of the security features of trustful smart cities' applications, which conforms with existing legislation and regulations.

Determining the ambient influences and configuration of optimised environments for emotional wellbeing of older adults

It is now widely recognised that aspects such as tiredness or mood state can have an impact on an individual's wellbeing. However, there also exist other less studied factors that might be influential, and whose analysis is important to maximise personal wellbeing. The aim of this study was to determine the influence of a set of 12 selected factors. Using the analysis of a 20-experiment case study by soft computing techniques the intention was to establish the most appropriate configuration for each factor to compose an optimal living environment to foster wellbeing. The analysis revealed that ambient lighting and stress level are the factors that most impact emotional wellbeing. To a lesser extent, being able to take a break, ambient temperature and ambient noise play a relatively determining role. The findings of this work can be used to establish a living environment for older persons that favours their emotional wellbeing. Practitioner summary: This study analyses the level of influence of a set of ambient factors on the emotional wellbeing of older people, conducting, to this end, a series of controlled experiments, and concluding that ambient lighting and stress level are the factors most relevant to promote a better living environment.KEY POINTSOlder adults' emotional interpretation of pictures depends on the environment and ambient factors.Ambient factors, such as lighting and stress, have a significant, positive effect on visual interpretation of stimuli and greater wellbeing.The use of soft computing techniques facilitates the quantification of the influence of factors affecting emotional wellbeing.

How Interpersonal Distance Between Avatar and Human Influences Facial Affect Recognition in Immersive Virtual Reality

Purpose: The purpose of this study was to determine the optimal interpersonal distance (IPD) between humans and affective avatars in facial affect recognition in immersive virtual reality (IVR). The ideal IPD is the one in which the humans show the highest number of hits and the shortest reaction times in recognizing the emotions displayed by avatars. The results should help design future therapies to remedy facial affect recognition deficits.

Methods: A group of 39 healthy volunteers participated in an experiment in which participants were shown 65 dynamic faces in IVR and had to identify six basic emotions plus neutral expression presented by the avatars. We decided to limit the experiment to five different distances: D1 (35 cm), D2 (55 cm), D3 (75 cm), D4 (95 cm), and D5 (115 cm), all belonging to the intimate and personal interpersonal spaces. Of the total of 65 faces, 13 faces were presented for each of the included distances. The views were shown at different angles: 50% in frontal view, 25% from the right profile, and 25% from the left profile. The order of appearance of the faces presented to each participant was randomized.

Results: The overall success rate in facial emotion identification was 90.33%, being D3 the IPD with the best overall emotional recognition hits, although statistically significant differences could not be found between the IPDs. Consistent with results obtained in previous studies, identification rates for negative emotions were higher with increasing IPD, whereas the recognition task improved for positive emotions when IPD was closer. In addition, the study revealed irregular behavior in the facial detection of the emotion surprise.

Conclusions: IVR allows us to reliably assess facial emotion recognition using dynamic avatars as all the IPDs tested showed to be effective. However, no statistically significant differences in facial emotion recognition were found among the different IPDs.

Psychosocial remediation in depressive disorders: A systematic review

BACKGROUND

Social functioning impairment has been described in several psychiatric illness, including depressive disorders. It is associated with a deterioration in global functioning and quality of life, thus there is a growing interest in psychosocial functioning remediation interventions. This systematic review aims to review all psychotherapeutic, pharmacological and biological social functioning interventions in depressive disorders.

METHODS

A systematic search was conducted on PubMed, PsycINFO and Scopus from the first articles to 2019 following the PRISMA guidelines. 72 original papers were extracted from an initial number of 1827, based on the selected eligibility criteria.

RESULTS

A growing body of research was observed in the last 10 years, with most studies showing a low level of scientific evidence. The main diagnosis found was major depressive disorder and the principal social cognition domains assessed were emotional processing and attributional style. The type of intervention most found was the pharmacological one, followed by psychotherapeutic interventions classified as "non-specific. The efficacy of treatments showed an improvement in depressive symptoms and positive results for emotional processing and attributional style.

LIMITATIONS

Because there is a lack of well-controlled designs and really few interventions focusing on its remediation, and low homogeneity on the assessment of social aspects across, a comparison of results and the extraction of general conclusions is quite difficult.

CONCLUSIONS

Although a promising body of literature has been developed in recent years on the improvement of psychosocial functioning in patients with depressive disorders, more studies are needed to clarify relevant aspects in this area.

Nursing Interventions to Facilitate the Grieving Process after Perinatal Death: A Systematic Review

Perinatal death is the death of a baby that occurs between the 22nd week of pregnancy (or when the baby weighs more than 500 g) and 7 days after birth. After perinatal death, parents experience the process of perinatal grief. Midwives and nurses can develop interventions to improve the perinatal grief process. The aim of this review was to determine the efficacy of nursing interventions to facilitate the process of grief as a result of perinatal death. A systematic review of the literature was carried out. Studies that met the selection criteria underwent a quality assessment using the Joanna Briggs Institute critical appraisal tool. Four articles were selected out of the 640 found. Two are quasi-experimental studies, and two are randomized controlled clinical studies. The interventions that were analyzed positively improve psychological self-concept and role functions, as well as mutual commitment, depression, post-traumatic stress and symptoms of grief. These interventions are effective if they are carried out both before perinatal loss and after it has occurred. The support of health professionals for affected parents, their participation in the loss, expressing feelings and emotions, using distraction methods, group sessions, social support, physical activity, and family education are some of the effective interventions.

Feeling of Safety and Comfort towards a Socially Assistive Unmanned Aerial Vehicle That Monitors People in a Virtual Home

Unmanned aerial vehicles (UAVs) represent a new model of social robots for home care of dependent persons. In this regard, this article introduces a study on people’s feeling of safety and comfort while watching the monitoring trajectory of a quadrotor dedicated to determining their condition. Three main parameters are evaluated: the relative monitoring altitude, the monitoring velocity and the shape of the monitoring path around the person (ellipsoidal or circular). For this purpose, a new trajectory generator based on a state machine, which is successfully implemented and simulated in MATLAB/Simulink^®, is described. The study is carried out with 37 participants using a virtual reality (VR) platform based on two modules, UAV simulator and VR Visualiser, both communicating through the MQTT protocol. The participants’ preferences have been a high relative monitoring altitude, a high monitoring velocity and a circular path. These choices are a starting point for the design of trustworthy socially assistive UAVs flying in real homes.

Differences between young and older adults in physiological and subjective responses to emotion induction using films

Emotional response in aging is typically studied using the dimensional or the discrete models of emotion. Moreover, it is typically studied using subjective or physiological variables but not using both perspectives simultaneously. Additionally, tenderness is neglected in emotion induction procedures with older adults, with the present work being the first to include the study of physiological tenderness using film clips. This study integrated two separate approaches to emotion research, comparing 68 younger and 39 older adults and using a popular set of film clips to induce tenderness, amusement, anger, fear, sadness and disgust emotions. The direction of subjective emotional patterns was evaluated with self-reports and that of physiological emotional patterns was evaluated with a wearable emotion detection system. The findings suggest a dual-process framework between subjective and physiological responses, manifested differently in young and older adults. In terms of arousal, the older adults exhibited higher levels of subjective arousal in negative emotions and tenderness while young adults showed higher levels of physiological arousal in these emotions. These findings yield information on the multidirectionality of positive and negative emotions, corroborating that emotional changes in the adult lifespan appear to be subject to the relevance of the emotion elicitor to each age group.

Arousal Detection in Elderly People from Electrodermal Activity Using Musical Stimuli

The detection of emotions is fundamental in many areas related to health and well-being. This paper presents the identification of the level of arousal in older people by monitoring their electrodermal activity (EDA) through a commercial device. The objective was to recognize arousal changes to create future therapies that help them to improve their mood, contributing to reduce possible situations of depression and anxiety. To this end, some elderly people in the region of Murcia were exposed to listening to various musical genres (flamenco, Spanish folklore, Cuban genre and rock/jazz) that they heard in their youth. Using methods based on the process of deconvolution of the EDA signal, two different studies were carried out. The first, of a purely statistical nature, was based on the search for statistically significant differences for a series of temporal, morphological, statistical and frequency features of the processed signals. It was found that Flamenco and Spanish Folklore presented the highest number of statistically significant parameters. In the second study, a wide range of classifiers was used to analyze the possible correlations between the detection of the EDA-based arousal level compared to the participants' responses to the level of arousal subjectively felt. In this case, it was obtained that the best classifiers are support vector machines, with 87% accuracy for flamenco and 83.1% for Spanish Folklore, followed by K-nearest neighbors with 81.4% and 81.5% for Flamenco and Spanish Folklore again. These results reinforce the notion of familiarity with a musical genre on emotional induction.

Deep Support Vector Machines for the Identification of Stress Condition from Electrodermal Activity

Early detection of stress condition is beneficial to prevent long-term mental illness like depression and anxiety. This paper introduces an accurate identification of stress/calm condition from electrodermal activity (EDA) signals. The acquisition of EDA signals from a commercial wearable as well as their storage and processing are presented. Several time-domain, frequency-domain and morphological features are extracted over the skin conductance response of the EDA signals. Afterwards, a classification is undergone by using several classical support vector machines (SVMs) and deep support vector machines (D-SVMs). In addition, several binary classifiers are also compared with SVMs in the stress/calm identification task. Moreover, a series of video clips evoking calm and stress conditions have been viewed by 147 volunteers in order to validate the classification results. The highest F1-score obtained for SVMs and D-SVMs are 83% and 92%, respectively. These results demonstrate that not only classical SVMs are appropriate for classification of biomarker signals, but D-SVMs are very competitive in comparison to other classification techniques. In addition, the results have enabled drawing useful considerations for the future use of SVMs and D-SVMs in the specific case of stress/calm identification.

Social cognition remediation interventions: A systematic mapping review

Background

Impairments in social cognition have been described in several psychiatric and neurological disorders. Given the importance of the relationship between social cognition and functioning and quality of life in these disorders, there is a growing interest in social cognition remediation interventions. The aim of this study was to carry out a systematic mapping review to describe the state of the art in social cognition training and remediation interventions.

Methods

Publications from 2006 to 2016 on social cognition interventions were reviewed in four databases: Scopus, PsycINFO, PubMed and Embase. From the initial result set of 3229 publications, a final total of 241 publications were selected.

Results

The study revealed an increasing interest in social cognition remediation interventions, especially in the fields of psychiatry and psychology, with a gradual growth in the number of publications. These were frequently published in high impact factor journals and underpinned by robust scientific evidence. Most studies were conducted on schizophrenia, followed by autism spectrum disorders. Theory of mind and emotional processing were the focus of most interventions, whilst a limited number of studies addressed attributional bias and social perception. Targeted interventions in social cognition were the most frequent practice in the selected papers, followed by non-specific treatment interventions and broad-based interventions.

Conclusions

Research in social cognition remediation interventions is growing. Further studies are needed on attributional bias and social perception remediation programs, while the comparative efficacy of different interventions also remains unclear.

Multi-Lag Analysis of Symbolic Entropies on EEG Recordings for Distress Recognition

Distress is a critical problem in developed societies given its long-term negative effects on physical and mental health. The interest in studying this emotion has notably increased during last years, being electroencephalography (EEG) signals preferred over other physiological variables in this research field. In addition, the non-stationary nature of brain dynamics has impulsed the use of non-linear metrics, such as symbolic entropies in brain signal analysis. Thus, the influence of time-lag on brain patterns assessment has not been tested. Hence, in the present study two permutation entropies denominated Delayed Permutation Entropy and Permutation Min-Entropy have been computed for the first time at different time-lags to discern between emotional states of calmness and distress from EEG signals. Moreover, a number of curve-related features were also calculated to assess brain dynamics across different temporal intervals. Complementary information among these variables was studied through sequential forward selection and 10-fold cross-validation approaches. According to the results obtained, the multi-lag entropy analysis has been able to reveal new significant insights so far undiscovered, thus notably improving the process of distress recognition from EEG recordings.

Digital Technology for Internet Access by Patients With Early-Stage Schizophrenia in Spain: Multicenter Research Study

Background

Digital technology and social networks are part of everyday life in the current internet age, especially among young people. To date, few studies have been published worldwide on the pattern of use of digital technology devices and applications in patients with early-stage schizophrenia and even fewer comparing them with healthy participants (not using data from general population surveys) from the same demographic areas. In Spain, no such study has been carried out.

Objective

The aim of this study was to analyze how patients with early-stage schizophrenia use internet and social networks compared with healthy participants matched by age and gender and also to examine which devices are utilized to access internet resources.

Methods

A cross-sectional, multicentric study was carried out through a semistructured interview asking about the use of digital technology devices and internet. The sample comprised 90 patients and 90 healthy participants. The semistructured interview was conducted on 30 outpatients and 30 healthy subjects in each of the 3 different cities (Madrid, Alicante, and Cuenca). Student t test was used for continuous variables and chi-square test for categorical variables. In the case of ordinal variables, nonparametric Mann-Whitney U and Kruskal-Wallis H tests for independent samples were performed to compare groups.

Results

The results indicated that a large proportion of patients with early-stage schizophrenia have access to different digital devices and use them frequently. In addition, both groups coincide in the order of preference and the purpose for which they use the devices. However, a lower frequency of use of most digital technology devices was detected in patients compared with healthy participants. In the case of some devices, this was due to the impossibility of access and not a lack of interest.

Conclusions

To our knowledge, this is the first study to analyze patterns of internet access and use of digital technology devices and applications in Spanish patients with early-stage schizophrenia compared with healthy participants from the same demographic areas. The results on significant access and use of digital technology and internet shown in this cross-sectional study will allow enhanced and more efficient treatment strategies to be planned, utilizing digital technology devices, for patients with early-stage schizophrenia.

Configurational Entropy in Multicomponent Alloys: Matrix Formulation from Ab Initio Based Hamiltonian and Application to the FCC Cr-Fe-Mn-Ni System

Configuration entropy is believed to stabilize disordered solid solution phases in multicomponent systems at elevated temperatures over intermetallic compounds by lowering the Gibbs free energy. Traditionally, the increment of configuration entropy with temperature was computed by time-consuming thermodynamic integration methods. In this work, a new formalism based on a hybrid combination of the Cluster Expansion (CE) Hamiltonian and Monte Carlo simulations is developed to predict the configuration entropy as a function of temperature from multi-body cluster probability in a multi-component system with arbitrary average composition. The multi-body probabilities are worked out by explicit inversion and direct product of a matrix formulation within orthonomal sets of point functions in the clusters obtained from symmetry independent correlation functions. The matrix quantities are determined from semi canonical Monte Carlo simulations with Effective Cluster Interactions (ECIs) derived from Density Functional Theory (DFT) calculations. The formalism is applied to analyze the 4-body cluster probabilities for the quaternary system Cr-Fe-Mn-Ni as a function of temperature and alloy concentration. It is shown that, for two specific compositions (Cr 25Fe 25Mn 25Ni 25 and Cr 18Fe 27Mn 27Ni 28), the high value of probabilities for Cr-Fe-Fe-Fe and Mn-Mn-Ni-Ni are strongly correlated with the presence of the ordered phases L1 2 -CrFe 3 and L1 0-MnNi, respectively. These results are in an excellent agreement with predictions of these ground state structures by ab initio calculations. The general formalism is used to investigate the configuration entropy as a function of temperature and for 285 different alloy compositions. It is found that our matrix formulation of cluster probabilities provides an efficient tool to compute configuration entropy in multi-component alloys in a comparison with the result obtained by the thermodynamic integration method. At high temperatures, it is shown that many-body cluster correlations still play an important role in understanding the configuration entropy before reaching the solid solution limit of high-entroy alloys (HEAs).

Pharmacological interventions in social cognition deficits: A systematic mapping review

Social cognition is an important research field in psychiatry due to its relevance in the functioning and quality of life of patients. The objective of this work is to conduct a systematic mapping review of pharmacological strategies for improving social cognition deficits. Publications from 2006 to 2016 were reviewed in Scopus, PsycINFO, PubMed, and Embase. From the initial 1059 publications obtained, a final number of 110 were selected. The results show an increasing interest in pharmacological approaches in different medical fields (especially psychiatry, pharmacology, and endocrinology, with schizophrenia and autism as the most studied disorders), as can be observed in the progressive increase in the number of publications, the high degree of scientific evidence, and the high impact factor of publications. However, it is also observed that most studies were conducted with oxytocin, psychostimulants, and antipsychotics (mainly risperidone and olanzapine), with few studies using other drugs. In the different social cognition domains, the majority of publications were focused on emotional processing or theory of mind, with few studies in other domains. Thus, this systematic mapping review shows that, even though there are increasing research activities, there are some important gaps to cover in future investigation.

Multiscale Entropy Analysis for Recognition of Visually Elicited Negative Stress From EEG Recordings

Automatic identification of negative stress is an unresolved challenge that has received great attention in the last few years. Many studies have analyzed electroencephalographic (EEG) recordings to gain new insights about how the brain reacts to both short- and long-term stressful stimuli. Although most of them have only considered linear methods, the heterogeneity and complexity of the brain has recently motivated an increasing use of nonlinear metrics. Nonetheless, brain dynamics reflected in EEG recordings often exhibit a multiscale nature and no study dealing with this aspect has been developed yet. Hence, in this work two nonlinear indices for quantifying regularity and predictability of time series from several time scales are studied for the first time to discern between visually elicited emotional states of calmness and negative stress. The obtained results have revealed the maximum discriminant ability of 86.35% for the second time scale, thus suggesting that brain dynamics triggered by negative stress can be more clearly assessed after removal of some fast temporal oscillations. Moreover, both metrics have also been able to report complementary information for some brain areas.

Human-Avatar Symbiosis for the Treatment of Auditory Verbal Hallucinations in Schizophrenia through Virtual/Augmented Reality and Brain-Computer Interfaces

This perspective paper faces the future of alternative treatments that take advantage of a social and cognitive approach with regards to pharmacological therapy of auditory verbal hallucinations (AVH) in patients with schizophrenia. AVH are the perception of voices in the absence of auditory stimulation and represents a severe mental health symptom. Virtual/augmented reality (VR/AR) and brain computer interfaces (BCI) are technologies that are growing more and more in different medical and psychological applications. Our position is that their combined use in computer-based therapies offers still unforeseen possibilities for the treatment of physical and mental disabilities. This is why, the paper expects that researchers and clinicians undergo a pathway toward human-avatar symbiosis for AVH by taking full advantage of new technologies. This outlook supposes to address challenging issues in the understanding of non-pharmacological treatment of schizophrenia-related disorders and the exploitation of VR/AR and BCI to achieve a real human-avatar symbiosis.

Neural Correlates of Phrase Quadrature Perception in Harmonic Rhythm: An EEG Study Using a Brain-Computer Interface

For the sake of establishing the neural correlates of phrase quadrature perception in harmonic rhythm, a musical experiment has been designed to induce music-evoked stimuli related to one important aspect of harmonic rhythm, namely the phrase quadrature. Brain activity is translated to action through electroencephalography (EEG) by using a brain-computer interface. The power spectral value of each EEG channel is estimated to obtain how power variance distributes as a function of frequency. The results of processing the acquired signals are in line with previous studies that use different musical parameters to induce emotions. Indeed, our experiment shows statistical differences in theta and alpha bands between the fulfillment and break of phrase quadrature, an important cue of harmonic rhythm, in two classical sonatas.

Electrodermal Activity Sensor for Classification of Calm/Distress Condition

This article introduces a new and unobtrusive wearable monitoring device based on electrodermal activity (EDA) to be used in health-related computing systems. This paper introduces the description of the wearable device capable of acquiring the EDA of a subject in order to detect his/her calm/distress condition from the acquired physiological signals. The lightweight wearable device is placed in the wrist of the subject to allow continuous physiological measurements. With the aim of validating the correct operation of the wearable EDA device, pictures from the International Affective Picture System are used in a control experiment involving fifty participants. The collected signals are processed, features are extracted and a statistical analysis is performed on the calm/distress condition classification. The results show that the wearable device solely based on EDA signal processing reports around 89% accuracy when distinguishing calm condition from distress condition.

Collaborative Computer-Assisted Cognitive Rehabilitation System

Recently have been proposed different physical and cognitive rehabilitation system that allow people with some disabilities to improve and recover some lost capabilities. All these systems allow to carry out these therapies at home proving patients the possibility to accomplish a better rehabilitation, due to the fact that they can practice at home and in a more controlled environment. But, it is not so common that these systems include some social features that reduce the feeling of social isolation of the patients. Thus, in this paper we present an adaptation of a previous proposal including some multiuser therapies that try include some social features and other aspect related to videogames that increases the motivation and makes the treatment funny.

Neural Correlates of Phrase Rhythm: An EEG Study of Bipartite vs. Rondo Sonata Form

This paper introduces the neural correlates of phrase rhythm. In short, phrase rhythm is the rhythmic aspect of phrase construction and the relationships between phrases. For the sake of establishing the neural correlates, a musical experiment has been designed to induce music-evoked stimuli related to phrase rhythm. Brain activity is monitored through electroencephalography (EEG) by using a brain-computer interface. The power spectral value of each EEG channel is estimated to obtain how power variance distributes as a function of frequency. Our experiment shows statistical differences in theta and alpha bands in the phrase rhythm variations of two classical sonatas, one in bipartite form and the other in rondo form.

A Review on Intelligent Monitoring and Activity Interpretation

This survey paper provides a tour of the various monitoring and activity interpretation frameworks found in the literature. The needs of monitoring and interpretation systems are presented in relation to the area where they have been developed or applied. Their evolution is studied to better understand the characteristics of current systems. After this, the main features of monitoring and activity interpretation systems are defined. 

Influence of Tempo and Rhythmic Unit in Musical Emotion Regulation

This article is based on the assumption of musical power to change the listener's mood. The paper studies the outcome of two experiments on the regulation of emotional states in a series of participants who listen to different auditions. The present research focuses on note value, an important musical cue related to rhythm. The influence of two concepts linked to note value is analyzed separately and discussed together. The two musical cues under investigation are tempo and rhythmic unit. The participants are asked to label music fragments by using opposite meaningful words belonging to four semantic scales, namely "Tension" (ranging from Relaxing to Stressing), "Expressiveness" (Expressionless to Expressive), "Amusement" (Boring to Amusing) and "Attractiveness" (Pleasant to Unpleasant). The participants also have to indicate how much they feel certain basic emotions while listening to each music excerpt. The rated emotions are "Happiness," "Surprise," and "Sadness." This study makes it possible to draw some interesting conclusions about the associations between note value and emotions.

Robust Decentralized Nonlinear Control for a Twin Rotor MIMO System

This article presents the design of a novel decentralized nonlinear multivariate control scheme for an underactuated, nonlinear and multivariate laboratory helicopter denominated the twin rotor MIMO system (TRMS). The TRMS is characterized by a coupling effect between rotor dynamics and the body of the model, which is due to the action-reaction principle originated in the acceleration and deceleration of the motor-propeller groups. The proposed controller is composed of two nested loops that are utilized to achieve stabilization and precise trajectory tracking tasks for the controlled position of the generalized coordinates of the TRMS. The nonlinear internal loop is used to control the electrical dynamics of the platform, and the nonlinear external loop allows the platform to be perfectly stabilized and positioned in space. Finally, we illustrate the theoretical control developments with a set of experiments in order to verify the effectiveness of the proposed nonlinear decentralized feedback controller, in which a comparative study with other controllers is performed, illustrating the excellent performance of the proposed robust decentralized control scheme in both stabilization and trajectory tracking tasks.

Generalized Proportional Integral Control for an Unmanned Quadrotor System

In this article, a generalized proportional integral (GPI) control approach is presented for regulation and trajectory tracking problems in a nonlinear, multivariable quadrotor system model. In the feedback control law, no asymptotic observers or time discretizations are needed in the feedback loop. The GPI controller guarantees the asymptotically and exponentially stable behaviour of the controlled quadrotor position and orientation, as well as the possibilities of carrying out trajectory tracking tasks. The simulation results presented in the paper show that the proposed method exhibits very good stabilization and tracking performance in the presence of atmospheric disturbances and noise measurements.