A study on affect model validity : nominal vs ordinal labels

The question of representing emotion computationally remains largely unanswered: popular approaches require annotators to assign a magnitude (or a class) of some emotional dimension, while an alternative is to focus on the relationship between two or more options. Recent evidence in affective computing suggests that following a methodology of ordinal annotations and processing leads to better reliability and validity of the model. This paper compares the generality of classification methods versus preference learning methods in predicting the levels of arousal in two widely used affective datasets. Findings of this initial study further validate the hypothesis that approaching affect labels as ordinal data and building models via preference learning yields models of better validity.peer-reviewe

Abnormal auditory ERP N100 in children with dyslexia: comparison with their control siblings

<p>Abstract</p> <p>Background</p> <p>Recent research has implicated deficits of the working memory (WM) and attention in dyslexia. The N100 component of event-related potentials (ERP) is thought to reflect attention and working memory operation. However, previous studies showed controversial results concerning the N100 in dyslexia. Variability in this issue may be the result of inappropriate match up of the control sample, which is usually based exclusively on age and gender.</p> <p>Methods</p> <p>In order to address this question the present study aimed at investigating the auditory N100 component elicited during a WM test in 38 dyslexic children in comparison to those of 19 unaffected sibling controls. Both groups met the criteria of the International Classification of Diseases (ICD-10). ERP were evoked by two stimuli, a low (500 Hz) and a high (3000 Hz) frequency tone indicating forward and reverse digit span respectively.</p> <p>Results</p> <p>As compared to their sibling controls, dyslexic children exhibited significantly reduced N100 amplitudes induced by both reverse and forward digit span at Fp1, F3, Fp2, Fz, C4, Cz and F4 and at Fp1, F3, C5, C3, Fz, F4, C6, P4 and Fp2 leads respectively. Memory performance of the dyslexics group was not significantly lower than that of the controls. However, enhanced memory performance in the control group is associated with increased N100 amplitude induced by high frequency stimuli at the C5, C3, C6 and P4 leads and increased N100 amplitude induced by low frequency stimuli at the P4 lead.</p> <p>Conclusion</p> <p>The present findings are in support of the notion of weakened capture of auditory attention in dyslexia, allowing for a possible impairment in the dynamics that link attention with short memory, suggested by the anchoring-deficit hypothesis.</p

Mirror mirror on the wall... an unobtrusive intelligent multisensory mirror for well-being status self-assessment and visualization

A person’s well-being status is reflected by their face through a combination of facial expressions and physical signs. The SEMEOTICONS project translates the semeiotic code of the human face into measurements and computational descriptors that are automatically extracted from images, videos and 3D scans of the face. SEMEOTICONS developed a multisensory platform in the form of a smart mirror to identify signs related to cardio-metabolic risk. The aim was to enable users to self-monitor their well-being status over time and guide them to improve their lifestyle. Significant scientific and technological challenges have been addressed to build the multisensory mirror, from touchless data acquisition, to real-time processing and integration of multimodal data

Wize Mirror - a smart, multisensory cardio-metabolic risk monitoring system

In the recent years personal health monitoring systems have been gaining popularity, both as a result of the pull from the general population, keen to improve well-being and early detection of possibly serious health conditions and the push from the industry eager to translate the current significant progress in computer vision and machine learning into commercial products. One of such systems is the Wize Mirror, built as a result of the FP7 funded SEMEOTICONS (SEMEiotic Oriented Technology for Individuals CardiOmetabolic risk self-assessmeNt and Self-monitoring) project. The project aims to translate the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, multispectral images, and 3D scans of the face. The multisensory platform, being developed as the result of that project, in the form of a smart mirror, looks for signs related to cardio-metabolic risks. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. This paper is focused on the description of the part of that system, utilising computer vision and machine learning techniques to perform 3D morphological analysis of the face and recognition of psycho-somatic status both linked with cardio-metabolic risks. The paper describes the concepts, methods and the developed implementations as well as reports on the results obtained on both real and synthetic datasets

Detecting Complexity Abnormalities in Dyslexia Measuring Approximate Entropy of Electroencephalographic Signals

Dyslexia constitutes a specific reading disability, a condition characterized by severe difficulty in the mastery of reading despite normal intelligence or adequate education. Electroencephalogram (EEG) signal may be able to play an important role in the diagnosis of dyslexia. The Approximate Entropy (ApEn) is a recently formulated statistical parameter used to quantify the regularity of a time series data of physiological signals. In this paper, we initially estimated the ApEn values in signals recorded from controls subjects and dyslectic children. These values were firstly used for the statistical analysis of the two groups and secondly as feature input in a classification scheme. We also used the cross-ApEn methodology to get a measure of the asynchrony of the signals recorded from different electrodes. This preliminary study provides promising results towards correct identification of dyslexic cases, analyzing the corresponding EEG signals

Circulating FGF21 vs. Stress Markers in Girls during Childhood and Adolescence, and in Their Caregivers: Intriguing Inter-Relations between Overweight/Obesity, Emotions, Behavior, and the Cared-Caregiver Relationship

Fibroblast growth factor-21 (FGF21) acts on several brain regions, including the hypothalamic paraventricular nucleus, which is involved in the regulation of the hypothalamic-pituitary-adrenal (HPA) axis. The purpose of this study was to investigate the interrelations between FGF21 and stress indices in girls, as well as in their caregivers. 78 girls, aged between 5 and 15 years, were studied; 50 of them were overweight and obese (OB) and 28 in the control group (C). Serum FGF21 and hair and diurnal salivary cortisol were measured. Children participants filled in the Children&rsquo;s Depression Inventory (CDI) and the State-Trait Anxiety Inventory for Children (STAIC), while their caregivers filled in the State-Trait Anxiety Inventory (STAI), the Perceived Stress Scale (PSS), and the Holmes-Rahe Stress Events Scale (HRSES). The OB group girls had significantly higher levels of FGF21 than the C group (p &lt; 0.001). In contrast to the C group, in whom FGF21 levels were positively correlated with both hair and salivary AUCg cortisol concentrations (p = 0.045 and p = 0.007, respectively), no such correlations were observed in the OB group. In the caregivers of the OB group, STAI-state (r = 0.388, p = 0.008), STAI-trait (r = 0.4, p = 0.006), PSS (r = 0.388, p = 0.008), and HRSES (r = 0.358, p = 0.015) scores, all correlated positively with the FGF21 levels of the children under their care. FGF21 concentrations positively correlated with hair and salivary cortisol levels in the C group only. These findings may represent an interesting correlation dictated by bi-directional empathy links between the primary caregivers and the children under their care

Stress, Inflammation and Metabolic Biomarkers Are Associated with Body Composition Measures in Lean, Overweight, and Obese Children and Adolescents

The aim of this study was to examine the associations between multiple indices of stress, inflammation and metabolism vs. body composition parameters in 121 (43 boys, 78 girls) children and adolescents, aged 5–15 y. Subjects were divided into two groups: normal weight (N) (N = 40, BMI z-score = −0.1923 ± 0.6), and overweight/obese (OB) (N = 81, BMI z-score = 2.1947 ± 1.4). All subjects completed the State-Trait Anxiety Inventory for Children (STAIC) and Children’s Depression Inventory, and underwent cortisol measurements in hair, diurnal series of saliva, and morning serum. Circulating concentrations of high sensitivity C-reactive protein (hsCRP) and other inflammation biomarkers were also obtained. Body composition analysis was performed with a clinically validated, advanced bioimpedance apparatus (BIA), while heart rate variability (HRV) was measured as a stress biomarker by photoplethysmography (PPG). The OB group had a higher STAIC-state score, waist-to-hip ratio, skeletal muscle mass, and total and abdominal fat mass, and a lower percent fat-free mass (FFM) and bone density than the N group. HRV did not differ between the groups. In the entire population, percent fat mass correlated strongly with circulating hsCRP (r = 0.397, p = 0.001), ferritin, and other inflammatory biomarkers, as well as with indices of insulin resistance. A strong correlation between serum hsCRP and hair cortisol was also observed (r = 0.777, p < 0.001), suggesting interrelation of chronic stress and inflammation. Thus, body fat accumulation in children and adolescents was associated with an elevation in clinical and laboratory biomarkers of stress, inflammation, and insulin resistance. BIA-ACC and PPG can be utilized as a direct screening tool for assessing overweight- and obesity -related health risks in children and adolescents

Characterization of evoked and induced activity in EEG and assessment of intertrial variability

Summarization: Brain response to an internal or external event, is composed by the superposition of evoked and induced oscillatory activity, which reflect different brain mechanisms involved. Identification of such activations could serve for diagnostic purposes and provide useful tools for brain computer interfaces through insight on the activation of different brain regions. In this paper we study several statistical measures that have been proposed for identifying the nature of the involved activations. All these measures are based on some mean of an appropriate signal attribute over trials in the time/ frequency domain and do not characterize the variability across trials. In order to quantify trial-to-trial variability we consider a measure based on entropy, characterizing the distribution of power across trials. The results indicate that brain activations can be characterized and differentiated by their behavior from trial to trial.Presented on