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Multimodal prediction of trait emotional intelligence-Through affective changes measured using non-contact based physiological measures.

ABSTRACT: Inability to efficiently deal with emotionally laden situations, often leads to poor interpersonal interactions. This adversely affects the individual's psychological functioning. A higher trait emotional intelligence (EI) is not only associated with psychological wellbeing, educational attainment, and job-related success, but also with willingness to seek professional and non-professional help for personal-emotional problems, depression and suicidal ideation. Thus, it is important to identify low (EI) individuals who are more prone to mental health problems than their high EI counterparts, and give them the appropriate EI training, which will aid in preventing the onset of various mood related disorders. Since people may be unaware of their level of EI/emotional skills or may tend to fake responses in self-report questionnaires in high stake situations, a system that assesses EI using physiological measures can prove affective. We present a multimodal method for detecting the level of trait Emotional intelligence using non-contact based autonomic sensors. To our knowledge, this is the first work to predict emotional intelligence level from physiological/autonomic (cardiac and respiratory) response patterns to emotions. Trait EI of 50 users was measured using Schutte Self Report Emotional Intelligence Test (SSEIT) along with their cardiovascular and respiratory data, which was recorded using FMCW radar sensor both at baseline and while viewing affective movie clips. We first examine relationships between users' Trait EI scores and autonomic response and reactivity to the clips. Our analysis suggests a significant relationship between EI and autonomic response and reactivity. We finally attempt binary EI level detection using linear SVM. We also attempt to classify each sub factor of EI, namely-perception of emotion, managing own emotions, managing other's emotions, and utilization of emotions. The proposed method achieves an EI classification accuracy of 84%, while accuracies ranging from 58 to 76% is achieved for recognition of the sub factors. This is the first step towards identifying EI of an individual purely through physiological responses. Limitation and future directions are discussed.

SUBMITTER: Prajapati V

PROVIDER: S-EPMC8270480 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Affective computing through physiological signals monitoring is currently a hot topic in the scientific literature, but also in the industry. Many wearable devices are being developed for health or wellness tracking during daily life or sports activity. Likewise, other applications are being proposed for the early detection of risk situations involving sexual or violent aggressions, with the identification of panic or fear emotions. The use of other sources of information, such as video or audio signals will make multimodal affective computing a more powerful tool for emotion classification, improving the detection capability. There are other biological elements that have not been explored yet and that could provide additional information to better disentangle negative emotions, such as fear or panic. Catecholamines are hormones produced by the adrenal glands, two small glands located above the kidneys. These hormones are released in the body in response to physical or emotional stress. The main catecholamines, namely adrenaline, noradrenaline and dopamine have been analysed, as well as four physiological variables: skin temperature, electrodermal activity, blood volume pulse (to calculate heart rate activity. i.e., beats per minute) and respiration rate. This work presents a comparison of the results provided by the analysis of physiological signals in reference to catecholamine, from an experimental task with 21 female volunteers receiving audiovisual stimuli through an immersive environment in virtual reality. Artificial intelligence algorithms for fear classification with physiological variables and plasma catecholamine concentration levels have been proposed and tested. The best results have been obtained with the features extracted from the physiological variables. Adding catecholamine's maximum variation during the five minutes after the video clip visualization, as well as adding the five measurements (1-min interval) of these levels, are not providing better performance in the classifiers.

Project description:Past research regarding the emotional characteristics of kibbutz children raised with communal sleeping arrangements (CSA) showed inconsistent findings for emotional difficulties, such as insecure attachment and lowered ability for intimacy. Only little research has examined these children's emotional characteristics in their adulthood. Trait emotional intelligence (trait EI) is a new concept measuring a constellation of correlated emotional traits that capture a person's typical way of processing emotional information and reacting to it. The present study examined if difficulties in intimacy and trait EI characterize adults who had been raised in CSA kibbutzim (Hebrew plural of kibbutz). Participants comprised 1185 adults, aged 30-79, of whom 735 were raised in CSA kibbutzim, and the rest did not grow up in a Kibbutz at all. The participants completed three self-report questionnaires: Schutte's Self-Report Emotional Intelligence Test (SSEIT) to measure trait EI; the Revised Adult Attachment Scale to measure adult attachment; and the Rosenberg Self-Esteem Questionnaire to measure self-esteem. Results showed that most of the kibbutz CSA participants revealed levels of intimacy and trait EI comparable to the non-kibbutz group. However, a significant difference was found among adults with low self-esteem and poor attachment quality: For these participants, trait EI and intimacy were significantly lower for the CSA kibbutz group than for the non-kibbutz group. Furthermore, the CSA kibbutz group reported poorer non-verbal communication skills than the non-kibbutz group. We suggest that a minority of the CSA kibbutz children were less resilient to the kibbutz ecology. These children might have been more vulnerable to the effects of limited social options, decreased parental support, and the kibbutz education's ideological demands. For these children, the effects of communal upbringing on intimacy and attachment appear to be long-lasting, extending throughout their adulthood.

Project description:As a form of attention, mindfulness is qualitatively receptive and non-reactive, and is thought to facilitate adaptive emotional responding. One suggested mechanism is that mindfulness facilitates disengagement from an affective stimulus and thereby decreases affective reactivity. However, mindfulness has been conceptualized as a state, intervention, and trait. Because evidence is mixed as to whether self-reported trait mindfulness decreases affective reactivity, we used a multi-method approach to study the relationship between individual differences in self-reported trait mindfulness and electrocortical, electrodermal, electromyographic, and self-reported responses to emotional pictures. Specifically, while participants (N = 51) passively viewed pleasant, neutral, and unpleasant IAPS pictures, we recorded high-density (128 channels) electrocortical, electrodermal, and electromyographic data to the pictures as well as to acoustic startle probes presented during the pictures. Afterwards, participants rated their subjective valence and arousal while viewing the pictures again. If trait mindfulness spontaneously reduces general emotional reactivity, then for individuals reporting high rather than low mindfulness, response differences between emotional and neutral pictures would show relatively decreased early posterior negativity (EPN) and late positive potential (LPP) amplitudes, decreased skin conductance responses, and decreased subjective ratings for valence and arousal. High mindfulness would also be associated with decreased emotional modulation of startle eyeblink and P3 amplitudes. Although results showed clear effects of emotion on the dependent measures, in general, mindfulness did not moderate these effects. For most measures, effect sizes were small with rather narrow confidence intervals. These data do not support the hypothesis that individual differences in self-reported trait mindfulness are related to spontaneous emotional responses during picture viewing.

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Multimodal prediction of trait emotional intelligence-Through affective changes measured using non-contact based physiological measures.

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