Bipolarity in ear biometrics

Identifying people using their biometric data is a problem that is getting increasingly more attention. This paper investigates a method that allows the matching of people in the context of victim identification by using their ear biometric data. A high quality picture (taken professionally) is matched against a set of low quality pictures (family albums). In this paper soft computing methods are used to model different kinds of uncertainty that arise when manually annotating the pictures. More specifically, we study the use of bipolar satisfaction degrees to explicitly handle the bipolar information about the available ear biometrics

Functional cerebral asymmetries of emotional processes in the healthy and bipolar brain

The perception and processing of emotions are of primary importance for social interaction, which confers faculties such as inferring what another person’s feels. Brain organisation of emotion perception has shown to primarily involve right hemisphere functioning. However, the brain may be functionally organised according to fundamental aspects of emotion such as valence, rather than involving processing of emotions in general. It should be noted, however, that emotion perception is not merely a perceptual process consisting in the input of emotional information, but also involves one’s emotional response. Therefore, the functional brain organisation of emotional processing may also be influenced by emotional experience. An experimental model for testing functional cerebral asymmetries (FCAs) of valenced emotional experience is uniquely found in bipolar disorder (BD) involving impaired ability to regulate emotions and eventually leading to depressive or manic episodes. Previous models have only explained hemispheric asymmetries for manic and depressive mood episodes, but not for BD euthymia. The present thesis sought to investigate FCAs in emotional processing in two major ways. First, FCAs underlying facial emotion perception under normal functioning was examined in healthy controls. Secondly, functional brain organisation in emotional processing was further investigated by assessing FCAs in the bipolarity continuum, used as an experimental model for studying the processing of emotions. In contrast with previous asymmetry models, results suggested a right hemisphere involvement in emotional experience regardless of valence. Atypical FCAs were found in euthymic BD patients reflecting inherent aspects of BD functional brain organisation that are free of symptomatic influence. Also, BD patients exhibited atypical connectivity in a default amygdala network particularly affecting the right hemisphere, suggesting intrinsic mechanisms associated with internal emotional states. Last, BD patients were associated with a reduced right hemisphere specialisation in visuospatial attention, therefore suggesting that right hemisphere dysfunction can also affect non-emotional processes. Taken together, the findings emphasize a BD continuum model relying on euthymia as a bridging state between usual mood and acute mood phases

Experimental Setup and Protocol for Creating an EEG-signal Database for Emotion Analysis Using Virtual Reality Scenarios

Automatic emotion recognition systems aim to identify human emotions from physiological signals, voice, facial expression or even physical activity. Among these types of signals, the usefulness of signals from electroencephalography (EEG) should be highlighted. However, there are few publicly accessible EEG databases in which the induction of emotions is performed through virtual reality (VR) scenarios. Recent studies have shown that VR has great potential to evoke emotions in an effective and natural way within a laboratory environment. This work describes an experimental setup developed for the acquisition of EEG signals in which the induction of emotions is performed through a VR environment. Participants are introduced to the VR environment via head-mounted displays (HMD) and 14-channel EEG signals are collected. The experiments carried out with 12 participants (5 male and 7 female) are also detailed, with promising results, which allow us to think about the future development of our own dataset.Fondo Europeo de Desarrollo Regional (FEDER). Junta de Andalucía. P20 01173Gobierno de España. Ministerio de Ciencia Innovación y Universidades. Agencia Estatal de Investigación. TEC2017-82807-PGobierno de España. Ministerio de Ciencia Innovación y Universidades. Agencia Estatal de Investigación MCIN/AEI/ 10.13039/501100011033. PID2021-123090NB-I0

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review

Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals. The present comprehensive review concentrate on the two mental disorders: Major depressive Disorder (MDD) and Bipolar Disorder (BD) with noteworthy publications during the last ten years. There is a big need nowadays for phenotypic characterization of psychiatric disorders with biomarkers. Electroencephalography (EEG) signals could offer a rich signature for MDD and BD and then they could improve understanding of pathophysiological mechanisms underling these mental disorders. In this review, we focus on the literature works adopting neural networks fed by EEG signals. Among those studies using EEG and neural networks, we have discussed a variety of EEG based protocols, biomarkers and public datasets for depression and bipolar disorder detection. We conclude with a discussion and valuable recommendations that will help to improve the reliability of developed models and for more accurate and more deterministic computational intelligence based systems in psychiatry. This review will prove to be a structured and valuable initial point for the researchers working on depression and bipolar disorders recognition by using EEG signals.Comment: 29 pages,2 figures and 18 Table

Face and Feeling: An Examination of the Role of Facial Feedback in Emotion

The facial feedback hypothesis suggests that an individual’s experience of emotion is influenced by their facial expressions. Researchers, however, currently face conflicting narratives about whether this hypothesis is valid. A large replication effort consistently failed to replicate a seminal demonstration of the facial feedback hypothesis, but meta-analysis suggests the effect is real. To address these conflicting narratives, the Many Smiles Collaboration was formed. In the Many Smiles Collaboration, a large team of researchers—some advocates of the facial feedback hypothesis, some critics, and some without strong belief—collaborated to specify the best ways to test this hypothesis. Two pilot tests suggested that smiling could both initiate feelings of happiness in otherwise non-emotional contexts and magnify ongoing feelings of happiness. A conceptual replication revealed that scowling could initiate feelings of anger but did not provide evidence that scowling could magnify ongoing feeling of anger. An integrative framework for studying facial feedback effects—the Facial Feedback Component Process Framework—is reviewed

Sexually dimorphic subcortical brain volumes in emerging psychosis

In schizophrenic psychoses, the normal sexual dimorphism of the brain has been shown to be disrupted or even reversed. Little is known, however, at what time point in emerging psychosis this occurs. We have therefore examined, if these alterations are already present in the at-risk mental state (ARMS) for psychosis and in first episode psychosis (FEP) patients.; Data from 65 ARMS (48 (73.8%) male; age=25.1±6.32) and 50 FEP (37 (74%) male; age=27±6.56) patients were compared to those of 70 healthy controls (HC; 27 (38.6%) male; age=26±4.97). Structural T1-weighted images were acquired using a 3 Tesla magnetic resonance imaging (MRI) scanner. Linear mixed effects models were used to investigate whether subcortical brain volumes are dependent on sex.; We found men to have larger total brain volumes (p<0.001), and smaller bilateral caudate (p=0.008) and hippocampus volume (p<0.001) than women across all three groups. Older subjects had more GM and WM volume than younger subjects. No significant sex×group interaction was found.; In emerging psychosis there still seem to exist patterns of normal sexual dimorphism in total brain and caudate volume. The only structure affected by reversed sexual dimorphism was the hippocampus, with women showing larger volumes than men even in HC. Thus, we conclude that subcortical volumes may not be primarily affected by disrupted sexual dimorphism in emerging psychosis

Bayesian Analysis of Varying Coefficient Models and Applications

The varying coefficient models have been very important analytic tools to study the dynamic pattern in biomedicine fields. Since nonparametric varying coefficient models make few assumptions on the specification of the model, the 'curse of dimensionality' is an very important issue. Nonparametric Bayesian methods combat the curse of dimensionality through specifying a sparseness-favoring structure. This is accomplished through the Bayesian penalty for model complexity (Jeffreys and Berger, 1992) and is aided through centering on a base Bayesian parametric model. This dissertation presents three novel semiparametric Bayesian methods for the analysis of longitudinal data, diffusion tensor imaging data, and longitudinal circumplex data. In longitudinal data analysis, we propose a semiparametric Bayes approach to allow the impact of the predictors to vary across subjects, which allows flexibly local borrowing of information across subjects. Local hypothesis testing and confidence bands are developed for the identification of time windows for significant predictor impact, adjusting for multiple comparisons. The methods are assessed using simulation studies and applied to a yeast cell-cycle gene expression data set. In analyzing diffusion tensor imaging data, we propose a semiparametric Bayesian local functional model to connect multiple diffusion properties along white matter fiber bundles with a set of covariates of interest. An LPP2 prior facilitates global and local borrowing of information among subjects, while an infinite factor model flexibly represents low-dimensional structure. Local hypothesis testing and confidence bands are developed to identify fiber segments for significant association of covariates with multiple diffusion properties, controlling for multiple comparisons. The method is assessed by a simulation study and illustrated via two fiber tract data sets for neurodevelopment. In analyzing longitudinal circumplex data, we propose a semiparametric Bayesian infinite state-space circumplex model to capture the dynamic transition pattern of affective experience, where affects are characterized as an ordering on the circumference of a circle. A sticky infinite state hidden Markov model via hierarchical Dirichlet proces is used to address the time related state-switching structure and the self-transition feature. The method is assessed by a simulation study and an emotion data set for the dynamics of emotion regulation

Changing Choices

Changing choices psychological relativity theory unifying theory transformation parameters psychology psygologie koornstra choice dynamics The book contains a unifying theory on how the common object space is metrically transformed by individuals with different transformation parameters, due to their other previous experiences, to individually different psychological spaces for judgment on the one hand and preference on the other hand. Individual experiences also change generally, whereby the psychological spaces also change generally for each individual. The theory, therefore, is a psychological relativity theory of perception, judgment, preference, and choice dynamics. This book is a must read for all behavioural, economic, and social scientists with theoretical interest and some understanding of multidimensional data analyses. It integrates more than twenty theories on perception, judgment, preference, and risk decisions into one mathematical theory. Knowledge of advanced mathematics and modern geometry is not needed, because the mathematical subsections can be skipped without loss of understanding, due to their explanation and illustration by figures in the text

Digital Media and Textuality: From Creation to Archiving

Due to computers' ability to combine different semiotic modes, texts are no longer exclusively comprised of static images and mute words. How have digital media changed the way we write and read? What methods of textual and data analysis have emerged? How do we rescue digital artifacts from obsolescence? And how can digital media be used or taught inside classrooms? These and other questions are addressed in this volume that assembles contributions by artists, writers, scholars and editors. They offer a multiperspectival view on the way digital media have changed our notion of textuality