The development of detection methodologies for reliable drowsiness tracking is a challenging task requiring both appropriate signal inputs and accurate and robust algorithms of analysis. The aim of this research is to develop an advanced method to detect the drowsiness stage in electroencephalogram (EEG), the most reliable physiological measurement, using the promising Machine Learning methodologies. The methods used in this paper are based on Machine Learning methodologies such as stacked autoencoder with softmax layers. Results obtained from 62 volunteers indicate 100% accuracy in drowsy/wakeful discrimination, proving that this approach can be very promising for use in the next generation of medical devices. This methodology can be extended to other uses in everyday life in which the maintaining of the level of vigilance is critical. Future works aim to perform extended validation of the proposed pipeline with a wide-range training set in which we integrate the photoplethysmogram (PPG) signal and visual information with EEG analysis in order to improve the robustness of the overall approach.

Automatically recognizing negative emotions, such as anger or stress, and also positive ones, such as euphoria, can contribute to improving well-being. In real-life, emotion recognition is a difficult task since many of the technologies used for this purpose in both laboratory and clinic environments, such as electroencephalography (EEG) and electrocardiography (ECG), cannot realistically be used. Photoplethysmography (PPG) is a non-invasive technology that can be easily integrated into wearable sensors. This paper focuses on the comparison between PPG and ECG concerning their efficacy in detecting the psychophysical and affective states of the subjects. It has been confirmed that the levels of accuracy in the recognition of affective variables obtained by PPG technology are comparable to those achievable with the more traditional ECG technology. Moreover, the affective psychological condition of the participants (anxiety and mood levels) may influence the psychophysiological responses recorded during the experimental tests.

Cardiovascular disease is a leading cause of death. Several markers have been proposed to predict cardiovascular morbidity. The ankle-brachial index (ABI) marker is defined as the ratio between the ankle and the arm systolic blood pressures, and it is generally assessed through sphygmomanometers. An alternative tool for cardiovascular status assessment is Photoplethysmography (PPG). PPG is a non-invasive optical technique that measures volumetric blood changes induced by pulse pressure propagation within arteries. However, PPG does not provide absolute pressure estimation, making assessment of cardiovascular status less direct. The capability of a multivariate data-driven approach to predict ABI from peculiar PPG features was investigated here. ABI was measured using a commercial instrument (Enverdis Vascular Explorer, VE-ABI), and it was then used for a General Linear Model estimation of ABI from multi-site PPG in a supervised learning framework (PPG-ABI). A Receiver Operating Characteristic (ROC) analysis allowed to investigate the capability of PPG-ABI to discriminate cardiovascular impairment as defined by VE-ABI. Findings suggested that ABI can be estimated form PPG (r = 0.79) and can identify pathological cardiovascular status (AUC = 0.85). The advantages of PPG are simplicity, speed and operator-independency, allowing extensive screening of cardiovascular status and associated cardiovascular risks.

Peri-personal space (PPS) refers to the area immediately surrounding our body where interactions with objects and others occur. Immersive virtual reality (IVR) offers a controlled and adaptable environment, enabling the precise modulation of PPS boundaries. This provides significant benefits across various fields, including enhancing spatial awareness, advancing therapeutic interventions, and improving ergonomic designs. This systematic review aims to synthesize and evaluate the existing literature on this topic through various methodologies. To achieve this, three databases, PubMed, Scopus, and Web of Science, were searched following the PRISMA framework. Twenty studies met the eligibility criteria, were assessed for quality, and were included in the review. Across all studies, IVR was utilized to provide multisensory interactions and implement methods were used to manipulate PPS boundaries. The review categorizes PPS extension methods into three main domains: tool-use extension, extension related to tool use, social interaction extension, and embodiment-related extension. The findings confirm IVR’s potential to expand PPS boundaries and offer recommendations for leveraging this technology in future research. This work highlights the importance of IVR in advancing our understanding of PPS and its practical applications across diverse contexts.

Religious beliefs can shape how people process fear. Yet the psychophysiological mechanisms underlying this phenomenon remain poorly understood. We investigated fear learning and extinction processes in a group of individuals who professed a belief in God, compared to non-believers. Using a virtual reality Pavlovian fear conditioning/extinction task, we measured neurovegetative activity associated with these forms of associative learning. Our finding shows reduced fear extinction among God believers, compared to non-believers. This suggests that the general mechanism of fear extinction learning is suppressed in these individuals. Importantly, this effect was not explained by state or trait anxiety scores. These findings align with previous evidence linking religiosity and spirituality with the neural circuit of fear and suggest that religiosity may be associated with weaker inhibitory learning processes related to fear.

Background and Objectives: Theory of mind (ToM) deficits in children with ADHD are closely related to social difficulties and problems in interpersonal interactions. Evidence suggests that these cognitive deficits negatively affect the ability to understand and respond to others’ emotions and intentions, thus contributing to social isolation and a lower quality of life. However, the findings across studies vary, indicating that ADHD subtype and comorbidities, such as anxiety and mood disorders, can significantly influence sociocognitive deficits, modulating the extent of social problems. Materials and Methods: This review examines the relationship among ADHD, ToM, and empathy, analyzing studies comparing children with ADHD with peers with typical development or other neurodevelopmental conditions. A search in PubMed, Scopus, and the Cochrane Library prior to January 10, without time restrictions, using “ADHD”, “Cognitive Empathy”, and “Theory of Mind” identified relevant studies assessing these abilities through neuropsychological tests or questionnaires. Results: Of the initial 243 studies, 23 studies met the inclusion criteria. Children with ADHD exhibited significant impairments in ToM and empathy, affecting social cognition and interpersonal understanding. Various assessment tools revealed difficulties in understanding beliefs, emotions, and intentions, with executive function deficits playing a crucial role in shaping these social challenges. Conclusions: This review highlights the need for targeted therapeutic interventions that not only address cognitive deficits but consider emotional and metacognitive aspects, such as emotion regulation and self-awareness. Future research should focus on integrating executive function training with approaches that develop metacognitive and emotional skills, thus providing more comprehensive support.