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Background: Human body odors (BOs) serve as an effective means of social communication, with individuals exposed to emotional BOs experiencing a partial replication of the sender's affective state. This phenomenon may be particularly relevant in conditions where social interactions are impaired, such as social anxiety. Our study aimed to investigate if emotional human BOs could augment the benefits of mindfulness-based interventions., Methods: We enrolled 48 women with social anxiety symptoms and assigned them to groups exposed to happiness BO, fear BO, or clean air. Participants engaged in mindfulness practice over two consecutive days, which included breathing, meditation, and relaxation exercises. During these interventions, the odor specific to each group was presented. Affective symptoms were assessed at the beginning and end of each day, with heart rate variability (HRV) and skin conductance level (SCL) recorded during the intervention., Results: Self-reported anxiety level revealed a significant reduction in anxiety on the second day for both happiness and fear conditions, but not for the clean air group. However, on a physiological level, fear BO exposure compared to clean air led to decreased HRV, indicating that fear BO may induce a less physiological relaxed state. No significant differences were observed in SCL between odor conditions., Conclusions: These findings suggest that exposure to BOs triggers the perception of a "social presence", improving the ecological validity of a psychological treatment. If replicated and expanded, these findings could pave the way for using BOs as catalysts in existing therapies., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Patients with cancer often face depression and anxiety, and mindfulness-based interventions, including internet-based versions, can effectively reduce these symptoms and improve their quality of life. This study aims to investigate the impact of internet-based mindfulness-based interventions (e-MBIs) on anxiety, depression, and stress symptoms in patients with prostate or breast cancer., Objective: The primary aims are to assess the association between the amount and variety of e-MBI practices and symptom reduction. Second, this study aims to examine how baseline information such as sociodemographic characteristics, dispositional mindfulness (DM), and dispositional self-compassion (DSC) correlate with both app usage and symptom reduction., Methods: Participants included 107 patients with cancer (68 women with breast cancer and 38 men with prostate cancer) enrolled in a hospital setting. They were assigned to the intervention group of the NEVERMIND project, using the e-BMI module via the NEVERMIND app. A longitudinal design involved Pearson correlation analysis to determine the relationship between the amount and duration of e-MBI practices. Linear regression analysis was conducted to gauge the dose-response effect, evaluating the impact of DM and DSC on depression, anxiety, and stress. Negative binomial regression was conudcted to study sociodemographic factors' influence on the amount of practice in e-MBIs., Results: The participants with more diverse and sustained mindfulness practices experienced significant reductions in depression, anxiety, and stress. A high correlation (0.94) between e-MBI practices and symptom reduction was also highlighted. Male, married, and highly educated patients were more likely to engage in mindfulness. Even if DM and DSC did not impact the amount or variety of practices correlated, they were correlated with symptom reduction, showing that higher levels were associated with significant reductions in depression, anxiety, and stress., Conclusions: While more e-MBI practice is linked to reduced anxiety, depression, and stress, this study emphasizes the crucial role of variety of practice over amount. DM and DSC are key in shaping intervention effectiveness and may act as protectors against psychological distress. Using app log data, our research provides a unique perspective on e-MBI impact, contributing to cancer care understanding and guiding future studies., (© Francesca Malandrone, Sara Urru, Paola Berchialla, Pierre Gilbert Rossini, Francesco Oliva, Silvia Bianchi, Manuel Ottaviano, Sergio Gonzalez-Martinez, Vladimir Carli, Gaetano Valenza, Enzo Pasquale Scilingo, Sara Carletto, Luca Ostacoli. Originally published in JMIR Mental Health (https://mental.jmir.org).)

Objective: Infrared Thermography (IRT) has been used to monitor skin temperature variation in a contactless manner, in both clinical medicine and psychophysiology. Here, we introduce a new methodology to obtain information about autonomic correlates related to perspiration, peripheral vasomotility, and respiration from infrared recordings., Methods: Our approach involves a model-based decomposition of facial thermograms using Independent Component Analysis (ICA) and an ad-hoc preprocessing procedure. We tested our approach on 30 healthy volunteers whose psychophysiological state was stimulated as part of an experimental protocol., Results: Within-subject ICA analysis identified three independent components demonstrating correlations with the reference physiological signals. Moreover, a linear combination of independent components effectively predicted each physiological signal, achieving median correlations of 0.9 for electrodermal activity, 0.8 for respiration, and 0.73 for photoplethysmography peaks envelope. In addition, we performed a cross-validated inter-subject analysis, which allows to predict physiological signals from facial thermograms of unseen subjects., Conclusions/significance: Our findings validate the efficacy of features extracted from both original and thermal-derived signals for differentiating experimental conditions. This outcome emphasizes the sensitivity and promise of our approach, advocating for expanded investigations into thermal imaging within biomedical signal analysis. It underscores its potential for enhancing objective assessments of emotional states.

This research investigates the human-horse bond, aiming to unveil the physiological mechanisms regulating interspecies interactions. We hypothesized observing a physiological synchronization in human-horse dynamics, akin to human interactions. Through time-frequency Granger causality analysis of heart rate variability (HRV) and behavioral data, this study reveals the establishment of bidirectional synchronization in HRV between humans and horses. The coupling directionality is influenced by behavior and familiarity. In exploration scenarios led by horses, bidirectional interactions occur, particularly with familiar individuals. Conversely, during human-led activities such as grooming, physiological connectivity direction varies based on the familiarity level. In addition, the methodology allows in-depth analysis of sympathetic and parasympathetic nervous system contributions, highlighting their intricate role in the human-horse relationship. Such a physiological coupling estimate, correlated with behavioral data, provides a quantitative tool applicable across contexts and species This holds significant promise for assessing animal-assisted therapies and for applications in sports and various animal-related domains., Competing Interests: Authors declare no competing interests., (© 2024 The Author(s).)

Humans can decode emotional states from the body odors of the conspecifics and this type of emotional communication is particularly relevant in conditions in which social interactions are impaired, as in depression and social anxiety. The present study aimed to explore how body odors collected in happiness and fearful conditions modulate the subjective ratings, the psychophysiological response and the neural processing of neutral faces in individuals with depressive symptoms, social anxiety symptoms, and healthy controls (N = 22 per group). To this aim, electrocardiogram (ECG) and HD-EEG were recorded continuously. Heart Rate Variability (HRV) was extracted from the ECG as a measure of vagal tone, event-related potentials (ERPs) and event-related spectral perturbations (ERPSs) were extracted from the EEG. The results revealed that the HRV increased during the fear and happiness body odors conditions compared to clean air, but no group differences emerged. For ERPs data, repeated measure ANOVA did not show any significant effects. However, the ERPSs analyses revealed a late increase in delta power and a reduced beta power both at an early and a late stage of stimulus processing in response to the neutral faces presented with the emotional body odors, regardless of the presence of depressive or social anxiety symptoms. The current research offers new insights, demonstrating that emotional chemosignals serve as potent environmental cues. This represents a substantial advancement in comprehending the impact of emotional chemosignals in both individuals with and without affective disorders., (© 2024. The Author(s).)

The orienting reaction (OR) towards a new stimulus is subject to habituation, i.e., progressively attenuates with stimulus repetition. The skin conductance responses (SCRs) are known to represent a reliable measure of OR at the peripheral level. Yet, it is still a matter of debate which of the P3 subcomponents is the most likely to represent the central counterpart of the OR. The aim of the present work was to study habituation, recovery, and dishabituation phenomena intrinsic to a two-stimulus auditory oddball paradigm, one of the most-used paradigms both in research and clinic, by simultaneously recording SCRs and P3 in twenty healthy volunteers. Our findings show that the target stimulus was capable of triggering a more marked OR, as indexed by both SCRs and P3, compared to the standard stimulus, that could be due to its affective saliency and relevance for task completion; the application of temporal principal components analysis (PCA) to the P3 complex allowed us to identify several subcomponents including both early and late P3a (eP3a; lP3a), P3b, novelty P3 (nP3), and both a positive and a negative Slow Wave (+SW; -SW). Particularly, lP3a and P3b subcomponents showed a similar behavior to that observed for SCRs , suggesting them as central counterparts of OR. Finally, the P3 evoked by the first standard stimulus after the target showed a significant dishabituation phenomenon which could represent a sign of the local stimulus change. However, it did not reach a sufficient level to trigger an SCR/OR since it did not represent a salient event in the context of the task.

Physiological phenomena exhibit complex behaviours arising at multiple time scales. To investigate them, techniques derived from chaos theory were applied to physiological signals, providing promising results in distinguishing between healthy and pathological states. Fractal-like properties of electrodermal activity (EDA), a well-validated tool for monitoring the autonomic nervous system state, have been reported in previous literature. This study proposes the multiscale complexity index of electrodermal activity (MComEDA) to discern different autonomic responses based on EDA signals. This method builds upon our previously proposed algorithm, ComEDA, and it is empowered with a coarse-graining procedure to provide a view at multiple time scales of the EDA response. We tested MComEDA's performance on the EDA signals of two publicly available datasets, i.e., the Continuously Annotated Signals of Emotion (CASE) dataset and the Affect, Personality and Mood Research on Individuals and Groups (AMIGOS) dataset, both containing physiological data recorded from healthy participants during the view of ultra-short emotional video clips. Our results highlighted that the values of MComEDA were significantly different ( p -value < 0.05 after Wilcoxon signed rank test with Bonferroni's correction) when comparing high- and low-arousal stimuli. Furthermore, MComEDA outperformed the single-scale approach in discriminating among different valence levels of high-arousal stimuli, e.g., showing significantly different values for scary and amusing stimuli ( p -value = 0.024). These findings suggest that a multiscale approach to the nonlinear analysis of EDA signals can improve the information gathered on task-specific autonomic response, even when ultra-short time series are considered.

Background: Premature birth is known to affect the newborn's autonomic nervous system (ANS) maturation, with potential short and long-term impact on their neurobehavioral development. The purpose of the study was to investigate the effects of maternal directed singing and speaking on the preterm infants' autonomic nervous system (ANS) maturation as measured by the heart rate variability (HRV) parameters., Methods: In this multi-center randomized clinical trial, 30 stable preterm infants (m = 29,6 weeks of gestational age), without any abnormalities were randomized into an intervention (16) or a control group (14). HRV was measured weekly, for a total of 80 recordings during hospitalization, as well as before and after each session of singing or speaking., Results: The intervention group showed a significant increase of the percentage value of HRV power in the high frequency range when compared to the control group (p = 0.044). More specifically, the maternal singing significantly increased the high frequency power and decreased the low/high frequency power ratio (p = 0.037)., Conclusions: The preterm infant's vagal activity significantly increased in the intervention group, potentially enhancing their ANS maturation. The effect is specifically evidenced in the singing condition., Impact: Maternal singing affects the autonomic nervous system maturation of preterm hospitalized newborns in the NICU. No previous studies investigated how early vocal parental intervention can affect preterm infants developement, throught their autonomic nervous system maturation. Early Vocal Contact as an early intervention involving parents has a positive impact on preterm infant's development and it can be easily implemented in the care of preterm infants., Trial Registration: NCT04759573, retrospectively registered, 17 February 2021., (© 2023. The Author(s).)

Objective . This study presents a novel methodological approach for incorporating information related to the peripheral sympathetic response into the investigation of neural dynamics. Particularly, we explore how hedonic contextual olfactory stimuli influence the processing of neutral faces in terms of sympathetic response, event-related potentials and effective connectivity analysis. The objective is to investigate how the emotional valence of odors influences the cortical connectivity underlying face processing and the role of face-induced sympathetic arousal in this visual-olfactory multimodal integration. Approach . To this aim, we combine electrodermal activity (EDA) analysis and dynamic causal modeling to examine changes in cortico-cortical interactions. Results . The results reveal that stimuli arising sympathetic EDA responses are associated with a more negative N170 amplitude, which may be a marker of heightened arousal in response to faces. Hedonic odors, on the other hand, lead to a more negative N1 component and a reduced the vertex positive potential when they are unpleasant or pleasant. Concerning connectivity, unpleasant odors strengthen the forward connection from the inferior temporal gyrus (ITG) to the middle temporal gyrus, which is involved in processing changeable facial features. Conversely, the occurrence of sympathetic responses after a stimulus is correlated with an inhibition of this same connection and an enhancement of the backward connection from ITG to the fusiform face gyrus. Significance . These findings suggest that unpleasant odors may enhance the interpretation of emotional expressions and mental states, while faces capable of eliciting sympathetic arousal prioritize identity processing., (Creative Commons Attribution license.)

Background: eHealth systems have been increasingly used to manage depressive symptoms in patients with somatic illnesses. However, understanding the factors that drive their use, particularly among patients with breast and prostate cancer, remains a critical area of research., Objective: This study aimed to determine the factors influencing use of the NEVERMIND eHealth system among patients with breast and prostate cancer over 12 weeks, with a focus on the Technology Acceptance Model., Methods: Data from the NEVERMIND trial, which included 129 patients with breast and prostate cancer, were retrieved. At baseline, participants completed questionnaires detailing demographic data and measuring depressive and stress symptoms using the Beck Depression Inventory-II and the Depression, Anxiety, and Stress Scale-21, respectively. Over a 12-week period, patients engaged with the NEVERMIND system, with follow-up questionnaires administered at 4 weeks and after 12 weeks assessing the system's perceived ease of use and usefulness. Use log data were collected at the 2- and 12-week marks. The relationships among sex, education, baseline depressive and stress symptoms, perceived ease of use, perceived usefulness (PU), and system use at various stages were examined using Bayesian structural equation modeling in a path analysis, a technique that differs from traditional frequentist methods., Results: The path analysis was conducted among 100 patients with breast and prostate cancer, with 66% (n=66) being female and 81% (n=81) having a college education. Patients reported good mental health scores, with low levels of depression and stress at baseline. System use was approximately 6 days in the initial 2 weeks and 45 days over the 12-week study period. The results revealed that PU was the strongest predictor of system use at 12 weeks (β use at 12 weeks is predicted by PU at 12 weeks =.384), whereas system use at 2 weeks moderately predicted system use at 12 weeks (β use at 12 weeks is predicted by use at 2 weeks =.239). Notably, there were uncertain associations between baseline variables (education, sex, and mental health symptoms) and system use at 2 weeks, indicating a need for better predictors for early system use., Conclusions: This study underscores the importance of PU and early engagement in patient engagement with eHealth systems such as NEVERMIND. This suggests that, in general eHealth implementations, caregivers should educate patients about the benefits and functionalities of such systems, thus enhancing their understanding of potential health impacts. Concentrating resources on promoting early engagement is also essential given its influence on sustained use. Further research is necessary to clarify the remaining uncertainties, enabling us to refine our strategies and maximize the benefits of eHealth systems in health care settings., (©Nuhamin Gebrewold Petros, Jesper Alvarsson-Hjort, Gergö Hadlaczky, Danuta Wasserman, Manuel Ottaviano, Sergio Gonzalez-Martinez, Sara Carletto, Enzo Pasquale Scilingo, Gaetano Valenza, Vladimir Carli. Originally published in JMIR Cancer (https://cancer.jmir.org), 12.09.2023.)

The blinking rate far exceeds that required for moistening the cornea and changes depending on whether a person is resting or engaged in cognitive tasks. During ecological cognitive tasks (such as speaking, reading, and watching videos), blinks occur at breakpoints of attention suggesting a role in information segmentation, but the close relationship between cognition dynamics and blink timing still escapes a full understanding. The aim of the present study is to seek (1) if there is a temporal relationship between blink events and the consecutive steps of cognitive processing, and (2) if blink timing and the intensity of blink-related EEG responses are affected by task-relevance of stimuli. Our results show that, in a classical visual oddball task, (i) the occurrence of blinks is influenced by stimuli, irrespective of their relevance, (ii) blinks following relevant stimuli are only apparently delayed due to the need of finalizing a behavioural response, and (iii) stimulus relevance does not affect the intensity of the blink-related EEG response. This evidence reinforce the idea that blinks are not emitted until the last step of the processing sequence has been completed and suggests that blink-related EEG responses are generated by primary consciousness phenomena which are considered by their nature non-modulable (all-or-nothing) phenomena., (© 2023. Springer Nature Limited.)

The widespread popularity of Machine Learning (ML) models in healthcare solutions has increased the demand for their interpretability and accountability. In this paper, we propose the Physiologically-Informed Gaussian Process (PhGP) classification model, an interpretable machine learning model founded on the Bayesian nature of Gaussian Processes (GPs). Specifically, we inject problem-specific domain knowledge of inherent physiological mechanisms underlying the psycho-physiological states as a prior distribution over the GP latent space. Thus, to estimate the hyper-parameters in PhGP, we rely on the information from raw physiological signals as well as the designed prior function encoding the physiologically-inspired modelling assumptions. Alongside this new model, we present novel interpretability metrics that highlight the most informative input regions that contribute to the GP prediction. We evaluate the ability of PhGP to provide an accurate and interpretable classification on three different datasets, including electrodermal activity (EDA) signals collected during emotional, painful, and stressful tasks. Our results demonstrate that, for all three tasks, recognition performance is improved by using the PhGP model compared to competitive methods. Moreover, PhGP is able to provide physiological sound interpretations over its predictions.

Skin temperature reflects the Autonomic Nervous System (ANS)'s response to emotions and mental states and can be remotely measured using InfraRed Thermography. Understanding the physiological mechanisms that affect facial temperature is essential to improve the precision of emotional inference from thermal imaging. To achieve this aim, we recorded thermal images from 30 volunteers, at rest and under acute stress induced by the Stroop test, together with two autonomic correlates, i.e., heart rate variability and electrodermal activity, the former serving as a measure of cardiovascular dynamics, and the latter of the activity of the sweat glands. We used a Cross Mapping (CM) approach to quantify the nonlinear coupling of the temperature from four facial regions with the ANS correlates. CM reveals that facial temperature has a statistically significant correlation with the two autonomic time series, under both conditions, which was not evident in the linear domain. In particular, compared to the other regions, the nose shows a significantly higher link to the electrodermal activity in both conditions, and to the heart rate variability under stress. Moreover, the cardiovascular activity seems to be primarily responsible for the well-known decrease in nose temperature, and its coupling with the thermal signals significantly varies with gender.

Despite the notable recent developments in the field of remote photoplethysmography (rPPG), extracting a reliable pulse rate variability (PRV) signal still remains a challenge. In this study, eight image-based photoplethysmography (iPPG) extraction methods (GRD, AGRD, PCA, ICA, LE, SPE, CHROM, and POS) were compared in terms of pulse rate (PR) and PRV features. The algorithms were made robust for motion and illumination artifacts by using ad hoc pre- and postprocessing steps. Then, they were systematically tested on the public dataset UBFC-RPPG, containing data from 42 subjects sitting in front of a webcam (30 fps) while playing a time-sensitive mathematical game. The performances of the algorithms were evaluated by statistically comparing iPPG-based and finger-PPG-based PR and PRV features in terms of Spearman's correlation coefficient, normalized root mean square error (NRMSE), and Bland-Altman analysis. The study revealed POS and CHROM techniques to be the most robust for PR estimation and the assessment of overall autonomic nervous system (ANS) dynamics by using PRV features in time and frequency domains. Furthermore, we demonstrated that a reliable characterization of the vagal tone is made possible by computing the Poincaré map of PRV series derived from the POS and CHROM methods. This study supports the use of iPPG systems as promising tools to obtain clinically useful and specific information about ANS dynamics.

Previous studies have identified several brain regions involved in the sympathetic response and its integration with pain, cognition, emotions and memory processes. However, little is known about how such regions dynamically interact during a sympathetic activation task. In this study, we analyzed EEG activity and effective connectivity during a cold pressor test (CPT). A source localization analysis identified a network of common active sources including the right precuneus (r-PCu), right and left precentral gyri (r-PCG, l-PCG), left premotor cortex (l-PMC) and left anterior cingulate cortex (l-ACC). We comprehensively analyzed the network dynamics by estimating power variation and causal interactions among the network regions through the direct directed transfer function (dDTF). A connectivity pattern dominated by interactions in [Formula: see text] (8-12) Hz band was observed in the resting state, with r-PCu acting as the main hub of information flow. After the CPT onset, we observed an abrupt suppression of such [Formula: see text]-band interactions, followed by a partial recovery towards the end of the task. On the other hand, an increase of [Formula: see text]-band (1-4) Hz interactions characterized the first part of CPT task. These results provide novel information on the brain dynamics induced by sympathetic stimuli. Our findings suggest that the observed suppression of [Formula: see text] and rise of [Formula: see text] dynamical interactions could reflect non-pain-specific arousal and attention-related response linked to stimulus' salience., (© 2023. The Author(s).)

Non-specific sympathetic arousal responses to different stressful elicitations can be easily recognized from the analysis of physiological signals. However, neural patterns of sympathetic arousal during physical and mental fatigue are clearly not unitary. In the context of physiological monitoring through wearable and non-invasive devices, electrodermal activity (EDA) is the most effective and widely used marker of sympathetic activation. This study presents ComEDA, a novel approach for the characterization of complex dynamics of EDA. ComEDA overcomes the methodological limitations related to the application of nonlinear analysis to EDA dynamics, is not parameter-sensitive and is suitable for the analysis of ultra-short time series. We validated the proposed algorithm using synthetic series of white noise and 1/f noise, varying the number of samples from 50 to 5000. By applying our approach, we were able to discriminate a statistically significant increase of complexity in the 1/f noise with respect to white noise, obtaining p-values in the range [4.35 × 10 -6 , 0.03] after the Mann-Whitney test. Then, we tested ComEDA on both EDA signal and its tonic and phasic components, acquired from healthy subjects during four experimental protocols: two inducing a sympathetic activation through physical efforts and two based on mentally stressful tasks. Results are encouraging and promising, outperforming state of the art metrics such as the Sample Entropy. ComEDA shows good performance not only in discriminating between stressful tasks and resting state (p-value < 0.01 after the Wilcoxon non-parametric statistical test applied to EDA signals of all the four datasets), but also in differentiating different trends of complexity of EDA dynamics when induced by physical and mental stressors. These findings suggest future applications to automatically detect and selectively identify threats due to overwhelming stress impacting both physical and mental health or in the field of telemedicine to monitor autonomic diseases correlated to atypical sympathetic activation. The Matlab code implementing the ComEDA algorithm is available online., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Human body odors (HBOs) are powerful stimuli that can affect emotional, cognitive and behavioral processes. However, the characterization of the physiological response to HBOs is still to be fully investigated. Here, we analyzed the self-assessed emotion perception and the EEG event-related potentials (ERP) on 17 healthy young women during a simultaneous visual-olfactory stimulation. Particularly, we evaluated the effect of happiness and fear HBO on the amplitude of ERP waveforms elicited by neutral face processing. In addition, we evaluated the subjective valence and arousal perception of the presented neutral faces by means of the self-assessment-manikin test. We observed a significant increase in the amplitude of the late positive potential (LPP) for central left sites (i.e., C3) during the administration of HBOs with respect to clean air. On the other hand, we did not observe any significant change in the subjective valence and arousal scores as well as for the early components of the ERP (i.e., P100, N170, Vertex-Positive-Potential). Our preliminary results suggest that fear and happiness HBO can induce a protracted increase in the LPP, and possibly reflect an automatic and sustained engagement with emotionally significant content.

Reliably measuring fear perception could help evaluate the effectiveness of treatments for pathological conditions such as specific phobias or post-traumatic stress syndrome (e.g., exposure therapy). In this study, we developed a novel vir-tual reality (VR) scenario to induce fear and evaluate the related physiological response by the analysis of skin conductance (SC) signal. Eighteen subjects voluntarily experienced the fear VR scenario while their SC was recorded. After the experiment, each participant was asked to score the perceived subjective fear using a Likert scale from 1 to 10. We used the cvxEDA algorithm to process the collected SC signals and extract several features able to estimate the autonomic response to the fearful stimuli. Finally, the extracted features were linearly combined to model the subjective fear perception scores by means of LASSO linear regression. The sparsification imposed by the LASSO procedure to mitigate the overfitting risk identified an optimal linear model including only the standard deviation of the tonic SC component as a regressor (p = 0.007; R2 = 0.3337). The significant contribution of this feature to the model suggests that subjects experiencing more intense subjective fear have a more variable and unstable sympathetic tone.

Background: eHealth interventions have become a topic of interest in the field of mental health owing to their increased coordination and integration of different elements of care, in treating and preventing mental ill health in patients with somatic illnesses. However, poor usability, learnability, and user engagement might affect the effectiveness of an eHealth intervention. Identifying different sociodemographic characteristics that might be associated with higher perceived usability can help improve the usability of eHealth interventions., Objective: This study aimed to identify the sociodemographic characteristics that might be associated with the perceived usability of the NEVERMIND (Neurobehavioural Predictive and Personalised Modelling of Depressive Symptoms During Primary Somatic Diseases) eHealth system, comprising a mobile app and a sensorized shirt, in reducing comorbid depressive symptoms in patients with breast or prostate cancer., Methods: The study included a total of 129 patients diagnosed with breast (n=80, 62%) or prostate (n=49, 38%) cancer, who received a fully automated mobile app and sensorized shirt (NEVERMIND system). Sociodemographic data on age, sex, marital status, education level, and employment status were collected at baseline. Usability outcomes included the System Usability Scale (SUS), a subjective measure that covers different aspects of system usability; the user version of the Mobile App Rating Scale (uMARS), a user experience questionnaire; and a usage index, an indicator calculated from the number of days patients used the NEVERMIND system during the study period., Results: The analysis was based on 108 patients (n=68, 63%, patients with breast cancer and n=40, 37%, patients with prostate cancer) who used the NEVERMIND system for an average of 12 weeks and completed the study. The overall mean SUS score at 12 weeks was 73.4 (SD 12.5), which indicates that the NEVERMIND system has good usability, with no statistical differences among different sociodemographic characteristics. The global uMARS score was 3.8 (SD 0.3), and women rated the app higher than men (β=.16; P=.03, 95% CI 0.02-0.3), after adjusting for other covariates. No other sociodemographic characteristics were associated with higher uMARS scores. There was a statistical difference in the use of the NEVERMIND system between women and men. Women had significantly lower use (β=-0.13; P=.04, 95% CI -0.25 to -0.01), after adjusting for other covariates., Conclusions: The findings suggest that the NEVERMIND system has good usability according to the SUS and uMARS scores. There was a higher favorability of mobile apps among women than among men. However, men had significantly higher use of the NEVERMIND system. Despite the small sample size and low variability, there is an indication that the NEVERMIND system does not suffer from the digital divide, where certain sociodemographic characteristics are more associated with higher usability., Trial Registration: German Clinical Trials Register RKS00013391; https://www.drks.de/drks&#95;web/navigate.do?navigationId=trial.HTML&TRIAL&#95;ID=DRKS00013391., (©Nuhamin Gebrewold Petros, Gergo Hadlaczky, Sara Carletto, Sergio Gonzalez Martinez, Luca Ostacoli, Manuel Ottaviano, Björn Meyer, Enzo Pasquale Scilingo, Vladimir Carli. Originally published in JMIR Formative Research (https://formative.jmir.org), 08.06.2022.)

Background: This study assessed the effectiveness of the NEVERMIND e-health system, consisting of a smart shirt and a mobile application with lifestyle behavioural advice, mindfulness-based therapy, and cognitive behavioural therapy, in reducing depressive symptoms among patients diagnosed with severe somatic conditions. Our hypothesis was that the system would significantly decrease the level of depressive symptoms in the intervention group compared to the control group., Methods: This pragmatic, randomised controlled trial included 425 patients diagnosed with myocardial infarction, breast cancer, prostate cancer, kidney failure, or lower limb amputation. Participants were recruited from hospitals in Turin and Pisa (Italy), and Lisbon (Portugal), and were randomly assigned to either the NEVERMIND intervention or to the control group. Clinical interviews and structured questionnaires were administered at baseline, 12 weeks, and 24 weeks. The primary outcome was depressive symptoms at 12 weeks measured by the Beck Depression Inventory II (BDI-II). Intention-to-treat analyses included 425 participants, while the per-protocol analyses included 333 participants. This trial is registered in the German Clinical Trials Register, DRKS00013391., Findings: Patients were recruited between Dec 4, 2017, and Dec 31, 2019, with 213 assigned to the intervention and 212 to the control group. The sample had a mean age of 59·41 years (SD=10·70), with 44·24% women. Those who used the NEVERMIND system had statistically significant lower depressive symptoms at the 12-week follow-up (mean difference=-3·03, p <0·001; 95% CI -4·45 to -1·62) compared with controls, with a clinically relevant effect size (Cohen's d =0·39)., Interpretation: The results of this study show that the NEVERMIND system is superior to standard care in reducing and preventing depressive symptoms among patients with the studied somatic conditions., Funding: The NEVERMIND project received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 689691., Competing Interests: BM declares that he is employed by Gaia AG, the developer and owner of Deprexis, an internet-based intervention for depression, which is offered selectively to patients within the context of the NEVERMIND system. Other authors declare that they have no competing interests., (© 2022 The Authors.)

Thermal cameras capture the infrared radiation emitted from a body in a contactless manner and can provide an indirect estimation of the autonomic nervous system (ANS) dynamics through the regulation of the skin temperature. This study investigates the contribution given by thermal imaging for an effective automatic stress detection with the perspective of a contactless stress recognition system. To this aim, we recorded both ANS correlates (cardiac, electrodermal, and respiratory activity) and thermal images from 25 volunteers under acute stress induced by the Stroop test. We conducted a statistical analysis on the features extracted from each signal, and we implemented subject-independent classifications based on the support vector machine model with an embedded recursive feature elimination algorithm. Particularly, we trained three classifiers using different feature sets: the full set of features, only those derived from the peripheral autonomic correlates, and only those derived from the thermal images. Classification accuracy and feature selection results confirmed the relevant contribution provided by the thermal features in the acute stress detection task. Indeed, a combination of ANS correlates and thermal features achieved 97.37% of accuracy. Moreover, using only thermal features we could still successfully detect stress with an accuracy of 86.84% in a contact-free manner.

Background: Early parental interventions in the Neonatal Intensive Care Units (NICUs) have beneficial effects on preterm infants' short and long-term outcomes. The aim of this study was to investigate the effects of Early Vocal Contact (EVC)-singing and speaking-on preterm infants' vagal activity and autonomic nervous system (ANS) maturation., Methods: In this multi-center randomized clinical trial, twenty-four stable preterm infants, born at 25-32 weeks gestational age, were randomized to either the EVC group or control group, where mothers did not interact with the babies but observed their behavior. Heart Rate Variability (HRV) was acquired before intervention (pre-condition), during vocal contact, and after it (post condition)., Results: No significant effect of the vocal contact, singing and speaking, was found in HRV when the intervention group was compared to the control group. However, a significant difference between the singing and the pre and post conditions, respectively, preceding and following the singing intervention, was found in the Low and High Frequency power nu, and in the low/high frequency features ( p = 0.037). By contrast, no significant effect of the speaking was found., Conclusions: Maternal singing, but not speaking, enhances preterm infants' vagal activity in the short-term, thus improving the ANS stability. Future analyses will investigate the effect of enhanced vagal activity on short and long-term developmental outcomes of preterm infants in the NICU.

Dreams may be recalled after awakening from sleep following a defined electroencephalographic pattern that involves local decreases in low-frequency activity in the posterior cortical regions. Although a dreaming experience implies bodily changes at many organ, system, and timescale levels, the entity and causal role of such peripheral changes in a conscious dream experience are unknown. We performed a comprehensive, causal, multivariate analysis of physiological signals acquired during rapid eye movement (REM) sleep at night, including high-density electroencephalography and peripheral dynamics including electrocardiography and blood pressure. In this preliminary study, we investigated multiple recalls and nonrecalls of dream experiences using data from nine healthy volunteers. The aim was not only to investigate the changes in central and autonomic dynamics associated with dream recalls and nonrecalls, but also to characterize the central-peripheral dynamical and (causal) directional interactions, and the temporal relations of the related arousals upon awakening. We uncovered a brain-body network that drives a conscious dreaming experience that acts with specific interaction and time delays. Such a network is sustained by the blood pressure dynamics and the increasing functional information transfer from the neural heartbeat regulation to the brain. We conclude that bodily changes play a crucial and causative role in a conscious dream experience during REM sleep.

Photoplethysmography (PPG) is a completely noninvasive, optical method of assessing blood flow dynamics in peripheral vasculature. Wearable devices for PPG recording are becoming increasingly popular, due to their cost-effectiveness and ease of use. For these reasons, many recent scientific studies have proposed the use of pulse rate variability (PRV) extracted from PPG as a surrogate for heart rate variability (HRV), in monitoring autonomic activity and cardiovascular health.In this work, we used a cross-mapping approach, a methodology based on chaos theory, to compare PRV and HRV dynamics, and investigate their agreement according to age and gender of healthy subjects. We used ECG and PPG data acquired from 57 subjects (41 young and 16 elderly) during resting state in the supine position. Signals were gathered from the publicly available VORTAL dataset. Our results showed a statistically significant decrease of PRV reliability as an HRV surrogate in old participants, which was confirmed as significant when only men subjects were analyzed (p-value<0.01).Our findings, although preliminary, suggest greater caution in the use of PPG devices for monitoring cardiovascular health, especially in elderly men.

Olfaction and emotions share common networks in the brain. However, little is known on how the emotional content of odors modulate dynamically the cortico-cortical interactions within these networks. In this preliminary study, we investigated the effect of odor valence on effective connectivity through the use of Dynamic Causal Modeling (DCM). We recorded electroencephalographic (EEG) data from healthy subjects performing a passive odor task of odorants with different valence. Once defined a fully-connected a priori network comprising the pyriform cortex (PC), orbitofrontal cortex (OFC), and entorhinal cortex (EC), we tested the modulatory effect of odor valence on their causal interactions at the group level using the parametric empirical bayes (PEB) framework. Results show that both pleasant and the unpleasant odors have an inhibitory effect on the connection from EC to PC, whereas we did not observe any effect for the neutral odor. Moreover, the odor with positive valence has a stronger influence on connectivity dynamics compared to the negative odor. Although preliminary, our results suggest that odor valence can modulate brain connectivity.

This study proposes long wave infrared technology as a contactless alternative to wearable devices for stress detection. To this aim, we studied the change in facial thermal distribution of 17 healthy subjects in response to different stressors (Stroop Test, Mental Arithmetic Test). During the experimental sessions the electrodermal activity (EDA) and the facial thermal response were simultaneously recorded from each subject. It is well known from the literature that EDA can be considered a reliable marker for the psychological state variation, therefore we used it as a reference signal to validate the thermal results. Statistical analysis was performed to evaluate significant differences in the thermal features between stress and non-stress conditions, as well as between stress and cognitive load. Our results are in line with the outcomes of previous studies and show significant differences in the temperature trends over time between stress and resting conditions. As a new result, we found that the mean temperature changes of some less studied facial regions, e.g., the right cheek, are able not only to significantly discriminate between resting and stressful conditions, but also allow to recognize the typology of stressors. This outcome not only directs future studies to consider the thermal patterns of less explored facial regions as possible correlates of mental states, but more importantly it suggests that different psychological states could potentially be discriminated in a contactless manner.

Objective. The emotional response to olfactory stimuli implies the activation of a complex cascade of events triggered by structures lying in the limbic system. However, little is known about how this activation is projected up to cerebral cortex and how different cortical areas dynamically interact each other. Approach. In this study, we acquired EEG from human participants performing a passive odor-perception task with odorants conveying positive, neutral and negative valence. A novel methodological pipeline integrating global field power (GFP), independent component analysis (ICA), dipole source localization was applied to estimate effective connectivity in the challenging scenario of single-trial low-synchronized stimulation. Main results. We identified the brain network and the neural paths, elicited at different frequency bands, i.e.θ(4-7Hz),α(8-12Hz)andβ(13-30Hz), involved in odor valence processing. This brain network includes the orbitofrontal cortex (OFC), the cingulate gyrus (CgG), the superior temporal gyrus (STG), the posterior cingulate cortex/precuneus (PCC/PCu) and the parahippocampal gyrus (PHG). It was analyzed using a time-varying multivariate autoregressive model to resolve time-frequency causal interactions. Specifically, the OFC acts as the main node for odor perception and evaluation of pleasant and unpleasant stimuli, whereas no specific path was observed for a neutral stimulus. Significance. The results introduce new evidences on the role of the OFC during hedonic perception and underpin its specificity during the odor valence assessment. Our findings suggest that, after the odor onset different, bidirectional interactions occur between the OFC and other brain regions associated with emotion recognition/categorization and memory according to the stimulus valence. This outcome unveils how the hedonic olfactory network dynamically changes based on odor valence., (© 2021 IOP Publishing Ltd.)

Objective: Most of the bodily functions are regulated by multiple interactions between the parasympathetic (PNS) and sympathetic (SNS) nervous system. In this study, we propose a novel framework to quantify the causal flow of information between PNS and SNS through the analysis of heart rate variability (HRV) and electrodermal activity (EDA) signals., Methods: Our method is based on a time-varying (TV) multivariate autoregressive model of EDA and HRV time-series and incorporates physiologically inspired assumptions by estimating the Directed Coherence in a specific frequency range. The statistical significance of the observed interactions is assessed by a bootstrap procedure purposely developed to infer causalities in the presence of both TV model coefficients and TV model residuals (i.e., heteroskedasticity). We tested our method on two different experiments designed to trigger a sympathetic response, i.e., a hand-grip task (HG) and a mental-computation task (MC)., Results: Our results show a parasympathetic driven interaction in the resting state, which is consistent across different studies. The onset of the stressful stimulation triggers a cascade of events characterized by the presence or absence of the PNS-SNS interaction and changes in the directionality. Despite similarities between the results related to the two tasks, we reveal differences in the dynamics of the PNS-SNS interaction, which might reflect different regulatory mechanisms associated with different stressors., Conclusion: We estimate causal coupling between PNS and SNS through MVAR modeling of EDA and HRV time-series., Significance: Our results suggest promising future applicability to investigate more complex contexts such as affective and pathological scenarios.

Prostate cancer (PCa) gold-standard diagnosis relies on prostate biopsy, which is currently overly recommended since other available noninvasive tools such as prostate-specific antigen (PSA) multiparametric MRI (mMRI) showed low diagnostic accuracy or high costs, respectively. The aim of the study was to determine the accuracy of a novel Bioelectric Impedance Analysis (BIA) test endorectal probe for the selection of patients candidate to prostate biopsy and in particular the clinical value of three different parameters such as resistance (R), reactance (Xc), and phase angle (PA) degree. One-hundred twenty-three consecutive candidates to prostate biopsy and 40 healthy volunteers were enrolled. PSA and PSA density (PSAD) determinations, Digital Rectal Examination (DRE), and the novel BIA test were analyzed in patients and controls. A 16-core prostate biopsy was performed after a mMRI test. The study endpoints were to determine accuracy of BIA test in comparison with PSA, PSAD levels, and mMRI and obtain prostate cancer (PCa) prediction by BIA test. The Mann-Whitney U -test, the Wilkoxon rank test, and the Holm-Bonferroni's method were adopted for statistical analyses, and a computational approach was also applied to differentiate patients with PCa from those with benign disease. Combined PSA, PSAD, DRE, and trans-rectal ultrasound test failed to discern patients with PCa from those with benign disease (62.86% accuracy). mMRI PIRADS ≥3 showed a sensitivity of 83% and a specificity of 59%. The accuracy in discerning PCa increased up to 75% by BIA test (sensitivity 63.33% and specificity 83.75%). The novel finger probe BIA test is a cheap and reliable test that may help to improve clinical multifeature noninvasive diagnosis for PCa and reduce unnecessary biopsies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Bartoletti, Greco, Di Vico, Durante, Ficarra, Scilingo and Valenza.)

Understanding human trust in machine partners has become imperative due to the widespread use of intelligent machines in a variety of applications and contexts. The aim of this paper is to investigate whether human-beings trust a social robot-i.e. a human-like robot that embodies emotional states, empathy, and non-verbal communication-differently than other types of agents. To do so, we adapt the well-known economic trust-game proposed by Charness and Dufwenberg (2006) to assess whether receiving a promise from a robot increases human-trust in it. We find that receiving a promise from the robot increases the trust of the human in it, but only for individuals who perceive the robot very similar to a human-being. Importantly, we observe a similar pattern in choices when we replace the humanoid counterpart with a real human but not when it is replaced by a computer-box. Additionally, we investigate participants' psychophysiological reaction in terms of cardiovascular and electrodermal activity. Our results highlight an increased psychophysiological arousal when the game is played with the social robot compared to the computer-box. Taken all together, these results strongly support the development of technologies enhancing the humanity of robots.

Heartbeat regulation is achieved through different routes originating from central autonomic network sources, as well as peripheral control mechanisms. While previous studies successfully characterized cardiovascular regulatory mechanisms during a single stressor, to the best of our knowledge, a combination of multiple concurrent elicitations leading to the activation of different autonomic regulatory routes has not been investigated yet. Therefore, in this study, we propose a novel modeling framework for the quantification of heartbeat regulatory mechanisms driven by different neural routes. The framework is evaluated using two heartbeat datasets gathered from healthy subjects undergoing physical and mental stressors, as well as their concurrent administration. Experimental results indicate that more than 70% of the heartbeat regulatory dynamics is driven by the physical stressor when combining physical and cognitive/emotional stressors. The proposed framework provides quantitative insights and novel perspectives for neural activity on cardiac control dynamics, likely highlighting new biomarkers in the psychophysiology and physiopathology fields. A Matlab implementation of the proposed tool is available online.

Early differential diagnosis of several motor neuron diseases (MNDs) is extremely challenging due to the high number of overlapped symptoms. The routine clinical practice is based on clinical history and examination, usually accompanied by electrophysiological tests. However, although previous studies have demonstrated the involvement of altered metabolic pathways, biomarker-based monitoring tools are still far from being applied. In this study, we aim at characterizing and discriminating patients with involvement of both upper and lower motor neurons (i.e., amyotrophic lateral sclerosis (ALS) patients) from those with selective involvement of the lower motor neuron (LMND), by using blood data exclusively. To this end, in the last ten years, we built a database including 692 blood data and related clinical observations from 55 ALS and LMND patients. Each blood sample was described by 108 analytes. Starting from this outstanding number of features, we performed a characterization of the two groups of patients through statistical and classification analyses of blood data. Specifically, we implemented a support vector machine with recursive feature elimination (SVM-RFE) to automatically diagnose each patient into the ALS or LMND groups and to recognize whether they had a fast or slow disease progression. The classification strategy through the RFE algorithm also allowed us to reveal the most informative subset of blood analytes including novel potential biomarkers of MNDs. Our results show that we successfully devised subject-independent classifiers for the differential diagnosis and prognosis of ALS and LMND with remarkable average accuracy (up to 94%), using blood data exclusively.

A human-animal relationship can be developed through subsequent interactions, affected by the positive or negative emotional valence of the proceeding one. Horses implement a process of categorization to classify humans with whom they interact as positive, negative, or neutral stimuli by evaluating the kind of approach and the nature of the contact. In these terms, human-animal interactions are emotionally charged events, eliciting specific emotional states in both subjects involved. Although the human-horse relationship has been mainly investigated through behavioral analysis, physiological indicators are needed for a more objective assessment of the emotional responses. Heart rate variability (HRV) is a commonly used autonomic nervous system (ANS) correlate estimating the sympathovagal balance as a psychophysiological marker of emotion regulation in horses. We have assumed that long-term positive relationships with humans may have a positive and immediate impact on the emotional arousal of the horse, detectable, via ANS activity, during the interaction. We analyzed horses' heartbeat dynamics during their interaction with either familiar or unfamiliar handlers, applying a standardized experimental protocol consisting of three different conditions shifting from the absence of interaction to physical contact. The ANS signals were monitored through an innovative non-invasive wearable system, not interfering with the unconscious emotional response of the animal. We demonstrated that horses appeared to feel more relaxed while physically interacting (e.g., grooming on the right side) with some familiar handlers compared to the same task performed by someone unfamiliar. The shift of the sympathovagal balance toward a vagal predominance suggests that the horses experienced a decrease in stress response as a function not only of the handler's familiarity but also of the type of interaction they are experiencing. These results constitute the objective evidence of horses' capacity to individually recognize a familiar person, adding the crucial role of familiarity with the handler as a paramount component of human-animal interaction. Our rigorous methodological approach may provide a significant contribution to various fields such as animal welfare while further investigating the emotional side of the human-animal relationships., Competing Interests: AG, AL, and ES are founding partners of the company Feel-Ing S.r.l. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Scopa, Greco, Contalbrigo, Fratini, Lanatà, Scilingo and Baragli.)

The non-invasiveness of photoplethysmographic (PPG) acquisition systems, together with their cost-effectiveness and easiness of connection with IoT technologies, is opening up to the possibility of their widespread use. For this reason, the study of the reliability of PPG and pulse rate variability (PRV) signal quality has become of great scientific, technological, and commercial interest. In this field, sensor location has been demonstrated to play a crucial role. The goal of this study was to investigate PPG and PRV signal quality acquired from two body locations: finger and wrist. We simultaneously acquired the PPG and electrocardiographic (ECG) signals from sixteen healthy subjects (aged 28.5 ± 3.5, seven females) who followed an experimental protocol of affective stimulation through visual stimuli. Statistical tests demonstrated that PPG signals acquired from the wrist and the finger presented different signal quality indexes (kurtosis and Shannon entropy), with higher values for the wrist-PPG. Then we propose to apply the cross-mapping (CM) approach as a new method to quantify the PRV signal quality. We found that the performance achieved using the two sites was significantly different in all the experimental sessions ( p < 0.01), and the PRV dynamics acquired from the finger were the most similar to heart rate variability (HRV) dynamics.

Standard functional assessment of autonomic nervous system (ANS) activity on cardiovascular control relies on spectral analysis of heart rate variability (HRV) series. However, difficulties in obtaining a reliable measure of sympathetic activity from HRV spectra limits the exploitation of sympatho-vagal metrics. On the other hand, measures of electrodermal activity (EDA) have been demonstrated to provide a reliable quantifier of sympathetic dynamics. In this study we propose novel indices of phasic autonomic regulation mechanisms by combining HRV and EDA correlates and thoroughly investigating their time-varying dynamics. HRV and EDA series were gathered from 26 healthy subjects during a cold-pressor test and emotional stimuli. Instantaneous linear and nonlinear (bispectral) estimates of vagal dynamics were obtained from HRV through inhomogeneous point-process models, and combined with a sensitive maker of sympathetic tone from EDA spectral power. A wavelet decomposition analysis was applied to estimate phasic components of the proposed sympatho-vagal indices. Results show significant statistical differences for the proposed indices between the cold-pressor elicitation and previous resting state. Furthermore, an accuracy of 73.08% was achieved for the automatic emotional valence recognition. The proposed nonlinear processing of phasic ANS markers brings novel insights on autonomic functioning that can be exploited in the field of affective computing and psychophysiology.

Background: Depressive symptoms are common in individuals suffering from severe somatic conditions. There is a lack of interventions and evidence-based interventions aiming to reduce depressive symptoms in patients with severe somatic conditions. The aim of the NEVERMIND project is to address these issues and provide evidence by testing the NEVERMIND system, designed to reduce and prevent depressive symptoms in comparison to treatment as usual., Methods: The NEVERMIND study is a parallel-groups, pragmatic randomised controlled trial to assess the effectiveness of the NEVERMIND system in reducing depressive symptoms among individuals with severe somatic conditions. The NEVERMIND system comprises a smart shirt and a user interface, in the form of a mobile application. The system is a real-time decision support system, aiming to predict the severity and onset of depressive symptoms by modelling the well-being condition of patients based on physiological data, body movement, and the recurrence of social interactions. The study includes 330 patients who have a diagnosis of myocardial infarction, breast cancer, prostate cancer, kidney failure, or lower limb amputation. Participants are randomised in blocks of ten to either the NEVERMIND intervention or treatment as usual as the control group. Clinical interviews and structured questionnaires are administered at baseline, at 12 weeks, and 24 weeks to assess whether the NEVERMIND system is superior to treatment as usual. The endpoint of primary interest is Beck Depression Inventory II (BDI-II) at 12 weeks defined as (i) the severity of depressive symptoms as measured by the BDI-II. Secondary outcomes include prevention of the onset of depressive symptoms, changes in quality of life, perceived stigma, and self-efficacy., Discussion: There is a lack of evidence-based interventions aiming to reduce and prevent depressive symptoms in patients with severe somatic conditions. If the NEVERMIND system is effective, it will provide healthcare systems with a novel and innovative method to attend to depressive symptoms in patients with severe somatic conditions., Trial Registration: DRKS00013391. Registered 23 November 2017.

This study investigates the assessment of motor imagery (MI) ability in humans. Commonly, MI ability is measured through two methodologies: a self-administered questionnaire (MIQ-3) and the mental chronometry (MC), which measures the temporal discrepancy between the actual and the imagined motor tasks. However, both measures rely on subjects' self-assessment and do not use physiological measures. In this study, we propose a novel set of features extracted from the nonlinear dynamics of the eye gaze signal to discriminate between good and bad imagers. To this aim, we designed an experiment where twenty volunteers, categorized as good or bad imagers according to MC, performed three tasks: a motor task (MT), a visual Imagery task (VI), and a kinaesthetic Imagery task (KI). Throughout the experiment, the subjects' eye gaze was continuously monitored using an eye-tracking system. Eye gaze time series were analyzed through recurrence quantification analysis of the reconstructed phase space and compared between the two groups. Statistical results have shown how nonlinear eye behavior can express an inner dynamics of imagery mental process and may be used as a more objective and physiological-based measure of MI ability., (Copyright © 2020 Lanata, Sebastiani, Di Gruttola, Di Modica, Scilingo and Greco.)

Equine assisted interventions (EAIs) include all therapeutic interventions aimed at improving human wellbeing through the involvement of horses. Due to the prominent emotional involvement traditionally characterizing their relation with humans, horses developed sophisticated communicative skills, which fostered their ability to respond to human emotional states. In this review, we hypothesize that the proximate causation of successful interventions could be human-animal mutual coordination, through which the subjects bodily and, most importantly, emotionally come into contact. We propose that detecting emotions of other individuals and developing the capacity to fine-tune one's own emotional states accordingly (emotional transfer mechanism), could represent the key engine triggering the positive effects of EAIs. We provide a comprehensive analysis of horses' socio-emotional competences according to recent literature and we propose a multidisciplinary approach to investigate this inter-specific match. By considering human and horse as a unique coupling system during the interaction, it would be possible to objectively measure the degree of coordination through the analysis of physiological variables of both human and animal. Merging the state of art on human-horse relationship with the application of novel methodologies, could help to improve standardized protocols for animal assisted interventions, with particular regard to the emotional states of subjects involved.

In this study, we investigated brain dynamics from electroencephalographic (EEG) signals during affective tactile stimulation conveyed by the dynamical contact with different fabrics. Thirty-three healthy subjects (16 females) were enrolled to interact with a haptic device able to mimic caress-like stimuli conveyed by strips of different fabrics moved back and forth at different velocities. Specifically, two velocity levels (i.e., 9.4 and 65 mm/sec) and two kinds of fabric (i.e., burlap and silk) were selected to deliver pleasant and unpleasant affective elicitations, according to subjects' self-assessment. EEG power spectra and functional connectivity were then calculated and analyzed. Experimental results, reported in terms of p-value topographic maps, demonstrated that caresses administered through unpleasant fabrics increased brain activity in the θ (4-8 Hz), α (8-14 Hz), and β (14-30 Hz) bands, whereas the use of pleasant fabrics enhanced functional connections in specific areas (e.g., frontal, occipital, and temporal cortices) depending on the oscillations frequency and caressing velocity. Furthermore, we adopted K-NN algorithms to automatically recognize the pleasantness of the haptic stimulation at a single-subject level using EEG power spectra, achieving a recognition accuracy up to 74.24%. Finally, we showed how brain oscillation power in the α and β bands over contralateral frontal- and central-cortex were the most informative features characterizing the pleasantness of a tactile stimulus on the forearm.

Virtual reality is a powerful tool in human behaviour research. However, few studies compare its capacity to evoke the same emotional responses as in real scenarios. This study investigates psycho-physiological patterns evoked during the free exploration of an art museum and the museum virtualized through a 3D immersive virtual environment (IVE). An exploratory study involving 60 participants was performed, recording electroencephalographic and electrocardiographic signals using wearable devices. The real vs. virtual psychological comparison was performed using self-assessment emotional response tests, whereas the physiological comparison was performed through Support Vector Machine algorithms, endowed with an effective feature selection procedure for a set of state-of-the-art metrics quantifying cardiovascular and brain linear and nonlinear dynamics. We included an initial calibration phase, using standardized 2D and 360° emotional stimuli, to increase the accuracy of the model. The self-assessments of the physical and virtual museum support the use of IVEs in emotion research. The 2-class (high/low) system accuracy was 71.52% and 77.08% along the arousal and valence dimension, respectively, in the physical museum, and 75.00% and 71.08% in the virtual museum. The previously presented 360° stimuli contributed to increasing the accuracy in the virtual museum. Also, the real vs. virtual classifier accuracy was 95.27%, using only EEG mean phase coherency features, which demonstrates the high involvement of brain synchronization in emotional virtual reality processes. These findings provide an important contribution at a methodological level and to scientific knowledge, which will effectively guide future emotion elicitation and recognition systems using virtual reality., Competing Interests: The authors have declared that no competing interests exist.

The affective role of touch has opened new perspectives in human-machine interaction. This paper presents an emotion recognition algorithm to investigate the role of tactile stimuli conveyed through a wearable haptic system during affective reading. To this end, a group of 32 healthy volunteers underwent an emotional stimulation by reading affective texts, with and without the concurrent presence of pleasant haptic stimuli. Throughout the experiment, autonomic nervous system dynamics was quantified through heart rate variability (HRV) and electrodermal activity (EDA) analyses. EDA and HRV features were then used as input of a SVM-RFE learning algorithm for an automatic recognition of neutral and arousing texts. The affective recognition of the reading was performed in the presence or absence of the haptic stimulation. Results show that the affective perception induced by the neutral and arousing reading were discriminated with a significantly improved accuracy (+14.5%) when a caress-like haptic stimulus was conveyed to the user.

In this paper, we present a novel approach to dynamically describe human upper limb trajectories, addressing the question on whether and to which extent synergistic multi-joint behavior is observed and preserved over time evolution and across subjects. To this goal, we performed experiments to collect human upper limb joint angle trajectories and organized them in a dataset of daily living tasks. We then characterized the upper limb poses at each time frame through a technique that we named repeated-principal component analysis (R-PCA). We found that, although there is no strong evidence on the predominance of one principal component (PC) over the others, the subspace identified by the first three PCs takes into account most of the motion variability. We evaluated the stability of these results over time, showing that during the reaching phase, there is a strong consistency of these findings across participants. In other words, our results suggest that there is a time-invariant low-dimensional approximation of upper limb kinematics, which can be used to define a suitable reduced dimensionality control space for upper limb robotic devices in motion phases.

Current research in the emotion recognition field is exploring the possibility of merging the information from physiological signals, behavioural data, and speech. Electrodermal activity (EDA) is amongst the main psychophysiological arousal indicators. Nonetheless, it is quite difficult to be analyzed in ecological scenarios, like, for instance, when the subject is speaking. On the other hand, speech carries relevant information of subject emotional state and its potential in the field of affective computing is still to be fully exploited. In this work, we aim at exploring the possibility of merging the information from electrodermal activity (EDA) and speech to improve the recognition of human arousal level during the pronunciation of single affective words. Unlike the majority of studies in the literature, we focus on speakers' arousal rather than the emotion conveyed by the spoken word. Specifically, a support vector machine with recursive feature elimination strategy (SVM-RFE) is trained and tested on three datasets, i.e using the two channels (i.e., speech and EDA) separately and then jointly. The results show that the merging of EDA and speech information significantly improves the marginal classifier (+11.64%). The six selected features by the RFE procedure will be used for the development of a future multivariate model of emotions.

Previous studies have characterized the physiological interactions between central nervous system (brain) and peripheral cardiovascular system (heart) during affective elicitation in healthy subjects; however, questions related to the directionality of this functional interplay have been gaining less attention from the scientific community. Here, we explore brain-heart interactions during visual emotional elicitation in healthy subjects using measures of Granger causality (GC), a widely used descriptor of causal influences between two dynamical systems. The proposed approach inferences causality between instantaneous cardiovagal dynamics estimated from inhomogeneous point-process models of the heartbeat and high-density electroencephalogram (EEG) dynamics in 22 healthy subjects who underwent pleasant/unpleasant affective elicitation by watching pictures from the International Affective Picture System database. Particularly, we calculated the GC indexes between the EEG spectrogram in the canonical θ-, α-, β-, and γ-bands and both the instantaneous mean heart rate and its continuous parasympathetic modulations (i.e., the instantaneous HF power). Thus we looked for significant statistical differences among GC values estimated during the resting state, neutral elicitation, and pleasant/unpleasant arousing elicitation. As compared with resting state, coupling strength increases significantly in the left hemisphere during positive stimuli and in the right hemisphere during negative stimuli. Our results further reveal a correlation between emotional valence and lateralization of the dynamical information transfer going from brain-to-heart, mainly localized in the prefrontal, somatosensory, and posterior cortexes, and of the information transfer from heart-to-brain, mainly reflected into the fronto-parietal cortex oscillations in the γ-band (30 - 45 Hz).

Although a plethora of synthetic data generation models have been proposed to validate biomarkers of brain and cardiovascular dynamics separately, a limited number of computational methods estimating directed brain-heart information flow are currently available in the scientific literature. This study introduces a computational framework exploiting existing generative models for a novel time-resolved quantification of causal brain-heart interplay. Exemplarily, having electroencephalographic signals and heart rate variability series as inputs, respective synthetic data models are coupled through parametrised functions defined in accordance with current central autonomic network (CAN) knowledge. We validate this concept using data from 30 healthy volunteers undergoing notable sympathetic elicitation through a cold-pressor test, and further compare the obtained results with a state-of-the-art method as maximal information coefficient. Although our findings are in agreement with previous CAN findings, we report new insights into the role of fronto-parietal region activity and lateralisation mechanisms over the temporal cortices during prolonged peripheral elicitation, which occur with specific time delays. Additionally, the afferent autonomic outflow maps to brain oscillations in the δ and γ bands, whereas complementary cortical dynamics in the θ, α, and β bands act on efferent autonomic control. The proposed framework paves the way towards novel biomarker definitions for the assessment of complex physiological networks using existing data generation models for brain and peripheral dynamics.

Recent functional magnetic resonance imaging (fMRI) studies have identified specific neural patterns related to three different categories of movements: intransitive (i.e., meaningful gestures that do not include the use of objects), transitive (i.e., actions involving an object), and tool-mediated (i.e., actions involving a tool to interact with an object). However, fMRI intrinsically limits the exploitation of these results in a real scenario, such as a brain-machine interface. In this paper, we propose a new approach to automatically predict intransitive, transitive, or tool-mediated movements of the upper limb using electroencephalography (EEG) spectra estimated during a motor planning phase. To this end, high-resolution EEG data gathered from 33 healthy subjects were used as input of a three-class k-nearest neighbors classifier. Different combinations of EEG-derived spatial and frequency information were investigated to find the most accurate feature vector. In addition, we studied gender differences further splitting the dataset into only-male data, and only-female data. A remarkable difference was found between accuracies achieved with male and female data, the latter yielding the best performance (78.55% of accuracy for the prediction of intransitive, transitive, and tool-mediated actions). These results potentially suggest that different gender-based models should be employed for the future BMI applications.

Objective: The aim of the study was to investigate the effect of physicians' supportive communication on analogue patients' (APs) heart rate variability (HRV) and recall, while watching a video of palliative treatment being explained to a female patient., Methods: Sixty healthy women, acting as APs, were randomly assigned to watch one of two versions (standard vs. affective) of a scripted video-vignette of a bad news consultation to a female patient. The physician's communication differed only in the delivery of four supportive comments. Empathy, support and engagement perception were assessed by three questions. APs' HR was recorded during video-observation and recall was assessed immediately after. HRV was determined through measures defined in time and frequency domains., Results: Data of 54 APs (27 + 27) were included. The group with supportive communication perceived the physician as more empathic and supportive. Intra- and Inter-group comparisons suggested a greater sense of stress in the standard communication group. Recall did not differ in the two groups., Conclusion and Practice Implications: Findings show that the use of supportive expressions contribute to the perception of the physician as more empathic, potentially buffer patients' arousal after a bad news announcement, but does not confirm a positive impact on general recall., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Noninvasive, objective quantitative techniques to gauge emotional states are fundamental for clinical psychology as they overcome subjective bias of currently used questionnaires. To this end, we investigated brain oscillatory EEG activity during caresslike, affective haptic elicitation conveyed on the forearm at two force (strength of the caress) and three velocity (velocity of the caress) levels. Thirty-two healthy subjects (16 female) were asked to assess each haptic stimulus in terms of arousal (i.e., intensity of emotional perception) and valence (i.e., pleasantness/unpleasantness of emotional perception) scores, according to the circumplex model of affect. Changes in brain oscillations were quantified through spectral and functional connectivity analyses. EEG power spectra were estimated through the individual α frequency peak. Results, expressed in terms of p-value topographic maps, revealed a suppression of α, β, and γ oscillations over the contralateral somatosensory cortex during unpleasant caresses performed with the lowest force (2 N) and the highest velocity (65 mm/s). Conversely, pleasant caresses at the highest force were associated with a significant decrease of EEG oscillatory activity over the midfrontal region, at frequency bands including α, β, and γ. A correlation analysis showed that EEG γ power from the somatosensory area was linked to caressing force. The more unpleasant the affective cutaneous stimuli, the more the brain dynamics decrease in activity all over the scalp, primarily showing a suppression of α power over the midfrontal cortex. These results also pave the way for the design of haptic systems eliciting a given emotional state., (© 2018 Society for Psychophysiological Research.)

Affective Computing has emerged as an important field of study that aims to develop systems that can automatically recognize emotions. Up to the present, elicitation has been carried out with non-immersive stimuli. This study, on the other hand, aims to develop an emotion recognition system for affective states evoked through Immersive Virtual Environments. Four alternative virtual rooms were designed to elicit four possible arousal-valence combinations, as described in each quadrant of the Circumplex Model of Affects. An experiment involving the recording of the electroencephalography (EEG) and electrocardiography (ECG) of sixty participants was carried out. A set of features was extracted from these signals using various state-of-the-art metrics that quantify brain and cardiovascular linear and nonlinear dynamics, which were input into a Support Vector Machine classifier to predict the subject's arousal and valence perception. The model's accuracy was 75.00% along the arousal dimension and 71.21% along the valence dimension. Our findings validate the use of Immersive Virtual Environments to elicit and automatically recognize different emotional states from neural and cardiac dynamics; this development could have novel applications in fields as diverse as Architecture, Health, Education and Videogames.