Identification of Sympathetic Nervous System Activation From Skin Conductance: A Sparse Decomposition Approach With Physiological Priors

OBJECTIVE

Sweat secretions lead to variations in skin conductance (SC) signal. The relatively fast variation of SC, called the phasic component, reflects sympathetic nervous system activity. The slow variation related to thermoregulation and general arousal is known as the tonic component. It is challenging to decompose the SC signal into its constituents to decipher the encoded neural information related to emotional arousal.

METHODS

We model the phasic component using a second-order differential equation representing the diffusion and evaporation processes of sweating. We include a sparse impulsive neural signal that stimulates the sweat glands for sweat production. We model the tonic component with several cubic B-spline functions. We formulate an optimization problem with physiological priors on system parameters, a sparsity prior on the neural stimuli, and a smoothness prior on the tonic component. Finally, we employ a generalized-cross-validation-based coordinate descent approach to balance among the smoothness of the tonic component, the sparsity of the neural stimuli, and the residual.

RESULTS

We illustrate that we can successfully recover the unknowns separating both tonic and phasic components from both experimental and simulated data (with ). Further, we successfully demonstrate our ability to automatically identify the sparsity level for the neural stimuli and the smoothness level for the tonic component.

CONCLUSION

Our generalized-cross-validation-based novel method for SC signal decomposition successfully addresses previous challenges and retrieves a physiologically plausible solution.

SIGNIFICANCE

Accurate decomposition of SC could potentially improve cognitive stress tracking in patients with mental disorders.

Reduced activity, reactivity and functionality of the sympathetic nervous system in fibromyalgia: An electrodermal study

Alterations in autonomic activity are well established in fibromyalgia syndrome (FMS). Previous studies found reduced parasympathetic activity and sympathetic reactivity to physical and stress manipulations. However, sympathetic activity at rest has not been well studied in FMS. Sweating is exclusively controlled by sympathetic mechanisms. In this study, skin conductance (SC), as an indirect measure of sweating, was analyzed in 45 women with FMS and 38 healthy women. Tonic SC levels were recorded during a 4-minute rest period, and a breathing maneuver consisting of deep breathing with posterior breath holding was used to evoke SC responses. Associations of tonic SC with state anxiety and body temperature, measured in the hand, were explored to determine sweat functionality. The results showed reduced tonic SC levels, with a less marked decrease in SC during the recording period, and blunted SC reactivity to the breathing manipulation in FMS patients relative to healthy participants. Positive associations of SC with state anxiety and body temperature were observed in healthy participants, but these associations were absent in FMS patients. These results indicate alterations of sweating in FMS, suggesting reduced tonic and reactivity sympathetic influences. Furthermore, the absence of associations between SC levels and state anxiety and body temperature in the patient sample suggested a loss of functionality of the autonomic nervous system in FMS. Diminished autonomic regulation in FMS would reduce the ability to cope with environmental demands, thus favoring increases in stress and pain levels. Finally, the observed reduction in sweating is in accordance with evidence of small nerve fiber neuropathy in FMS.

The effects of verbal and imaginal worry on panic symptoms during an interoceptive exposure task

Previous research has documented the inhibitory effects of worry on cardiovascular reactivity to subsequently presented fear-relevant stimuli. Although theoretical assertions point to the verbal-linguistic (as opposed to imagery-based) nature of worry as the cause of these inhibitory effects, extant research investigating the effects of worrisome thinking on subsequent anxiety-eliciting tasks has not isolated the verbal-linguistic nature of worry as the active ingredient in its suppressive effects on arousal. Furthermore, prior research has not examined the potential effects of worry on maintenance of panic symptoms. In this study, participants high in anxiety sensitivity were asked to engage in verbal worry, imaginal worry, or relaxation prior to each of three repeated presentations of an interoceptive exposure task. Relaxation was associated with lower initial subjective fear that remained low across repeated exposures, and related stable sympathetic arousal (and decreased heart rate) over time. Imagery-based worry was associated with moderate initial subjective fear that was sustained across repeated exposures, and sympathetic arousal (and heart rate) that was likewise stable over time. However, verbal worry was associated with high initial subjective fear that was sustained over time, but sympathetic arousal (and heart rate) that decreased across repeated exposures. Thus, verbal worry was uniquely associated with a lack of synchronous response systems and maintenance of anxious meaning over time. Theoretical and clinical implications are discussed.

Physiological Signal Analysis and Classification of Stress from Virtual Reality Video Game

Stress can affect a person's performance and health positively and negatively. A lot of the relaxation methods have been suggested to reduce the amount of stress. This study used virtual reality (VR) video games to alleviate stress. Physiological signals captured from Electrocardiogram (ECG), galvanic skin response (GSR), and respiration (RESP) were used to determine if the subject was stressed or relaxed. Time and frequency domain features were then extracted to evaluate stress levels. Frequency domain methods such as low-frequency (LF), high-frequency (HF), LF-HF ratio (LF/HF) are considered the most effective for HRV analysis, Poincare plots are moré discerning visually and shares a 81% correlation with LF/HF ratio. GSR is associated with EDA activity, which only increases due to stress. Stress and relax were classified using Linear Discriminant Analysis (LDA), Decision Tree, Support Vector machine (SVM), Gradient Boost (GB), and Naive Bayes. GB performed the best with an accuracy of 85% after 5 fold cross validation with 100 iterations, which is admirable from a small dataset with 50 samples.

Towards Contactless Estimation of Electrodermal Activity Correlates

This paper presents a proof-of-concept for contactless and nonintrusive estimation of electrodermal activity (EDA) correlates using a camera. RGB video of the palm under three different lighting conditions showed that for a suitably chosen illumination strategy the data from the camera is sufficient to estimate EDA correlates which agree with the measurements done using laboratory grade physiological sensors. The effects we see in the recorded video can be attributed to sweat gland activity, which inturn is known to be correlated with EDA. These effects are so pronounced that simple pixel statistics can be used to quantify them. Such a method benefits from advances in computer vision and graphics research and has the potential to be used in affective computing and psychophysiology research where contact based sensors may not be suitable.

Exploration of the physiological response to an online gambling task by frequency domain analysis of the electrodermal activity

Online gambling has dramatically increased over the last decades, thus the study of the underlying physiological mechanisms could be helpful to better understand related disorders. Specifically, physiological arousal is well-known to play a key role in gambling behavior. In the present study, unconventional frequency feature of the electrodermal activity (EDA) was extracted (EDASymp_n) and compared to the most common heart rate variability (HRV) spectral parameters (LF, HF, HF_n, LF/HF) to measure arousal during an online gambling session. 46 subjects played online slot machines for 30 minutes, while EDA and ECG were recorded. In the analysis the gaming session was divided into three 10-minutes-long phases. A one-way repeated measures analysis of variance was carried out for each spectral parameter, with the game phases as within-subjects factor. All the calculated parameters showed significant differences between the initial phase of the game and the last two (p < 0.001). In particular, EDAsymp_n displayed a reciprocal trend with respect to HF_n: an initial increase (decrease for HF_n) was followed by a plateau phase. LF exhibited a significant difference also between the second and the third phases. EDA frequency-domain analysis appears to be a promising method for physiological arousal assessment, by showing the same discriminative power of HRV spectral components. Further research is needed to emphasize these findings.Clinical Relevance-This promotes the use of a new and easy-to-implement method to assess sympathetic activity.

Anxiety and the Neurobiology of Temporally Uncertain Threat Anticipation

When extreme, anxiety-a state of distress and arousal prototypically evoked by uncertain danger-can be debilitating. Uncertain anticipation is a shared feature of situations that elicit signs and symptoms of anxiety across psychiatric disorders, species, and assays. Despite the profound significance of anxiety for human health and wellbeing, the neurobiology of uncertain-threat anticipation remains unsettled. Leveraging a paradigm adapted from animal research and optimized for fMRI signal decomposition, we examined the neural circuits engaged during the anticipation of temporally uncertain and certain threat in 99 men and women. Results revealed that the neural systems recruited by uncertain and certain threat anticipation are anatomically colocalized in frontocortical regions, extended amygdala, and periaqueductal gray. Comparison of the threat conditions demonstrated that this circuitry can be fractionated, with frontocortical regions showing relatively stronger engagement during the anticipation of uncertain threat, and the extended amygdala showing the reverse pattern. Although there is widespread agreement that the bed nucleus of the stria terminalis and dorsal amygdala-the two major subdivisions of the extended amygdala-play a critical role in orchestrating adaptive responses to potential danger, their precise contributions to human anxiety have remained contentious. Follow-up analyses demonstrated that these regions show statistically indistinguishable responses to temporally uncertain and certain threat anticipation. These observations provide a framework for conceptualizing anxiety and fear, for understanding the functional neuroanatomy of threat anticipation in humans, and for accelerating the development of more effective intervention strategies for pathological anxiety.SIGNIFICANCE STATEMENT Anxiety-an emotion prototypically associated with the anticipation of uncertain harm-has profound significance for public health, yet the underlying neurobiology remains unclear. Leveraging a novel neuroimaging paradigm in a relatively large sample, we identify a core circuit responsive to both uncertain and certain threat anticipation, and show that this circuitry can be fractionated into subdivisions with a bias for one kind of threat or the other. The extended amygdala occupies center stage in neuropsychiatric models of anxiety, but its functional architecture has remained contentious. Here we demonstrate that its major subdivisions show statistically indistinguishable responses to temporally uncertain and certain threat. Collectively, these observations indicate the need to revise how we think about the neurobiology of anxiety and fear.

Pain Detection using a Smartphone in Real Time

We developed an objective real-time pain detection method using a smartphone and a wrist-worn wearable device to collect electrodermal activity (EDA) signals. Recently, various researchers have developed pain management applications. However, they rely on subjective self-reported pain scores or the video camera of a smartphone to detect pain, but the latter method's accuracy needs further improvement. In our work, we use a wrist-worn EDA device which transmits data via Bluetooth to a smartphone. A smartphone application was developed to analyze the EDA data so that near real-time processed pain detection information can be displayed. The analysis of EDA is based on estimating time-varying spectral power in the frequency range (0.08-0.24 Hz) associated with the sympathetic nervous system. This time-varying characterization of EDA is termed TVSymp. In this work, we also examined whether removing baseline EDA fluctuations from TVSymp would provide more accurate results. This was carried out by taking the moving average of the EDA response prior to stimulus and subtracting that value from the EDA response post stimulus. This approach is termed modified TVSymp (MTVSymp). Pain stimuli were induced in ten subjects using a thermal grill, which gives intense pain perception without damaging skin tissues. We compared both TVSymp and MTVSymp in detecting pain induced by the thermal grill using machine learning approaches. We found the accuracy of pain detection of TVSymp and MTVSymp to be 80% and 90%, respectively.

Autonomic and Electrophysiological Evidence for Reduced Auditory Habituation in Autism

It is estimated that nearly 90% of children on the autism spectrum exhibit sensory atypicalities. What aspects of sensory processing are affected in autism? Although sensory processing can be studied along multiple dimensions, two of the most basic ones involve examining instantaneous sensory responses and how the responses change over time. These correspond to the dimensions of 'sensitivity' and 'habituation'. Results thus far have indicated that autistic individuals do not differ systematically from controls in sensory acuity/sensitivity. However, data from studies of habituation have been equivocal. We have studied habituation in autism using two measures: galvanic skin response (GSR) and magneto-encephalography (MEG). We report data from two independent studies. The first study, was conducted with 13 autistic and 13 age-matched neurotypical young adults and used GSR to assess response to an extended metronomic sequence. The second study involved 24 participants (12 with an ASD diagnosis), different from those in study 1, spanning the pre-adolescent to young adult age range, and used MEG. Both studies reveal consistent patterns of reduced habituation in autistic participants. These results suggest that autism, through mechanisms that are yet to be elucidated, compromises a fundamental aspect of sensory processing, at least in the auditory domain. We discuss the implications for understanding sensory hypersensitivities, a hallmark phenotypic feature of autism, recently proposed theoretical accounts, and potential relevance for early detection of risk for autism.

Locus of emotion influences psychophysiological reactions to music

It is now widely accepted that the perception of emotional expression in music can be vastly different from the feelings evoked by it. However, less understood is how the locus of emotion affects the experience of music, that is how the act of perceiving the emotion in music compares with the act of assessing the emotion induced in the listener by the music. In the current study, we compared these two emotion loci based on the psychophysiological response of 40 participants listening to 32 musical excerpts taken from movie soundtracks. Facial electromyography, skin conductance, respiration and heart rate were continuously measured while participants were required to assess either the emotion expressed by, or the emotion they felt in response to the music. Using linear mixed effects models, we found a higher mean response in psychophysiological measures for the “perceived” than the “felt” task. This result suggested that the focus on one’s self distracts from the music, leading to weaker bodily reactions during the “felt” task. In contrast, paying attention to the expression of the music and consequently to changes in timbre, loudness and harmonic progression enhances bodily reactions. This study has methodological implications for emotion induction research using psychophysiology and the conceptualization of emotion loci. Firstly, different tasks can elicit different psychophysiological responses to the same stimulus and secondly, both tasks elicit bodily responses to music. The latter finding questions the possibility of a listener taking on a purely cognitive mode when evaluating emotion expression.

Arousal Detection in Elderly People from Electrodermal Activity Using Musical Stimuli

The detection of emotions is fundamental in many areas related to health and well-being. This paper presents the identification of the level of arousal in older people by monitoring their electrodermal activity (EDA) through a commercial device. The objective was to recognize arousal changes to create future therapies that help them to improve their mood, contributing to reduce possible situations of depression and anxiety. To this end, some elderly people in the region of Murcia were exposed to listening to various musical genres (flamenco, Spanish folklore, Cuban genre and rock/jazz) that they heard in their youth. Using methods based on the process of deconvolution of the EDA signal, two different studies were carried out. The first, of a purely statistical nature, was based on the search for statistically significant differences for a series of temporal, morphological, statistical and frequency features of the processed signals. It was found that Flamenco and Spanish Folklore presented the highest number of statistically significant parameters. In the second study, a wide range of classifiers was used to analyze the possible correlations between the detection of the EDA-based arousal level compared to the participants' responses to the level of arousal subjectively felt. In this case, it was obtained that the best classifiers are support vector machines, with 87% accuracy for flamenco and 83.1% for Spanish Folklore, followed by K-nearest neighbors with 81.4% and 81.5% for Flamenco and Spanish Folklore again. These results reinforce the notion of familiarity with a musical genre on emotional induction.

CNN and LSTM-Based Emotion Charting Using Physiological Signals

Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance improvement within a broader set of emotion classes in a less constrained real-world environment. To overcome these challenges, we propose a computational framework of 2D Convolutional Neural Network (CNN) architecture for the arrangement of 14 channels of EEG, and a combination of Long Short-Term Memory (LSTM) and 1D-CNN architecture for ECG and GSR. Our approach is subject-independent and incorporates two publicly available datasets of DREAMER and AMIGOS with low-cost, wearable sensors to extract physiological signals suitable for real-world environments. The results outperform state-of-the-art approaches for classification into four classes, namely High Valence-High Arousal, High Valence-Low Arousal, Low Valence-High Arousal, and Low Valence-Low Arousal. Emotion elicitation average accuracy of 98.73% is achieved with ECG right-channel modality, 76.65% with EEG modality, and 63.67% with GSR modality for AMIGOS. The overall highest accuracy of 99.0% for the AMIGOS dataset and 90.8% for the DREAMER dataset is achieved with multi-modal fusion. A strong correlation between spectral- and hidden-layer feature analysis with classification performance suggests the efficacy of the proposed method for significant feature extraction and higher emotion elicitation performance to a broader context for less constrained environments.

Towards the Applicability of Measuring the Electrodermal Activity in the Context of Process Model Comprehension: Feasibility Study

Process model comprehension is essential in order to understand the five Ws (i.e., who, what, where, when, and why) pertaining to the processes of organizations. However, research in this context showed that a proper comprehension of process models often poses a challenge in practice. For this reason, a vast body of research exists studying the factors having an influence on process model comprehension. In order to point research towards a neuro-centric perspective in this context, the paper at hand evaluates the appropriateness of measuring the electrodermal activity (EDA) during the comprehension of process models. Therefore, a preliminary test run and a feasibility study were conducted relying on an EDA and physical activity sensor to record the EDA during process model comprehension. The insights obtained from the feasibility study demonstrated that process model comprehension leads to an increased activity in the EDA. Furthermore, EDA-related results indicated significantly that participants were confronted with a higher cognitive load during the comprehension of complex process models. In addition, the experiences and limitations we learned in measuring the EDA during the comprehension of process models are discussed in this paper. In conclusion, the feasibility study demonstrated that the measurement of the EDA could be an appropriate method to obtain new insights into process model comprehension.

Physiological Sensors Based Emotion Recognition While Experiencing Tactile Enhanced Multimedia

Emotion recognition has increased the potential of affective computing by getting an instant feedback from users and thereby, have a better understanding of their behavior. Physiological sensors have been used to recognize human emotions in response to audio and video content that engages single (auditory) and multiple (two: auditory and vision) human senses, respectively. In this study, human emotions were recognized using physiological signals observed in response to tactile enhanced multimedia content that engages three (tactile, vision, and auditory) human senses. The aim was to give users an enhanced real-world sensation while engaging with multimedia content. To this end, four videos were selected and synchronized with an electric fan and a heater, based on timestamps within the scenes, to generate tactile enhanced content with cold and hot air effect respectively. Physiological signals, i.e., electroencephalography (EEG), photoplethysmography (PPG), and galvanic skin response (GSR) were recorded using commercially available sensors, while experiencing these tactile enhanced videos. The precision of the acquired physiological signals (including EEG, PPG, and GSR) is enhanced using pre-processing with a Savitzky-Golay smoothing filter. Frequency domain features (rational asymmetry, differential asymmetry, and correlation) from EEG, time domain features (variance, entropy, kurtosis, and skewness) from GSR, heart rate and heart rate variability from PPG data are extracted. The K nearest neighbor classifier is applied to the extracted features to classify four (happy, relaxed, angry, and sad) emotions. Our experimental results show that among individual modalities, PPG-based features gives the highest accuracy of 78.57% as compared to EEG- and GSR-based features. The fusion of EEG, GSR, and PPG features further improved the classification accuracy to 79.76% (for four emotions) when interacting with tactile enhanced multimedia.

Towards an anxiety and stress recognition system for academic environments based on physiological features

BACKGROUND AND OBJECTIVE

Traditional methods to determine stress and anxiety in academic environments consist of the application of questionnaires, but the main disadvantage is that the results depend on the students' self-perception. Being able to detect anxiety-related stress levels in a simple and objective way contributes greatly to dealing with low performance and school drop-out by students.

METHODS

The main contribution of this study is to identify the physiological features that could be used as predictors of stressful activities and states of anxiety in academic environments using an Arduino board and low-cost sensors. A test with 21 students was conducted, and a stress-inducing protocol was proposed and 21 physiological features of five signals were analyzed. In addition, the State-Trait Anxiety Inventory (STAI) was used to assess the level of anxiety for each student. Four classifiers were compared to find the physiological feature subset that provides the best accuracy to identify states of stress and anxiety.

RESULTS

The stress due to activities performed by students can be identified with an accuracy greater than 90% (Kappa = 0.84) using the k-Nearest Neighbors classifier, using data from heart rate, skin temperature and oximetry signals and four physiological features. Meanwhile, the identification of anxiety was achieved with an accuracy greater than 95% (Kappa = 0.90) using the SVM classifier with data from the galvanic skin response (GSR) signal and three physiological features.

CONCLUSIONS

The results provide a clue that anxiety detection in academic environments could be done using the analysis of physiological signals instead of STAI test scores. Besides, the results suggest that physiological features could be used to develop stress recognition systems to help teachers to identify the stressful tasks in an academic environment or to develop anxiety recognition systems to help students to control their level of anxiety when they are performing either academic tasks or exams.

Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning

This study attempted to multimodally measure mental workload and validate indicators for estimating mental workload. A simulated computer work composed of mental arithmetic tasks with different levels of difficulty was designed and used in the experiment to measure physiological signals (heart rate, heart rate variability, electromyography, electrodermal activity, and respiration), subjective ratings of mental workload (the NASA Task Load Index), and task performance. The indices from electrodermal activity and respiration had a significant increment as task difficulty increased. There were no significant differences between the average heart rate and the low-frequency/high-frequency ratio among tasks. The classification of mental workload using combined indices as inputs showed that classification models combining physiological signals and task performance can reach satisfying accuracy at 96.4% and an accuracy of 78.3% when only using physiological indices as inputs. The present study also showed that ECG and EDA signals have good discriminating power for mental workload detection. Practitioner summary: The methods used in this study could be applied to office workers, and the findings provide preliminary support and theoretical exploration for follow-up early mental workload detection systems, whose implementation in the real world could beneficially impact worker health and company efficiency. Abbreviations: NASA-TLX: the national aeronautics and space administration-task load index; ECG: electrocardiographic; EDA: electrodermal activity; EEG: electroencephalogram; LDA: linear discriminant analysis; SVM: support vector machine; KNN: k-nearest neighbor; ANNs: artificial neural networks; EMG: electromyography; PPG: photoplethysmography; SD: standard deviation; BMI: body mass index; DSSQ: dundee stress state questionnaire; ANOVA: analysis of variance; SC: skin conductance; RMS: root mean square; AVHR: the average heart rate; HR: heart rate; LF/HF: the ratio between the low frequencies band and the high frequency band; PSD: power spectral density; MF: median frequency; HRV: heart rate variability; BPNN: backpropagation neural network.

Effects of Daily Probiotics Supplementation on Anxiety Induced Physiological Parameters among Competitive Football Players

Competitive football players who undergo strenuous training and frequent competitions are more vulnerable to psychological disorders. Probiotics are capable of reducing these psychological disorders. The present study aimed to determine the effect of daily probiotics supplementation on anxiety induced physiological parameters among competitive football players. The randomized, double-blinded, placebo-controlled trial was conducted on 20 male footballers who received either probiotics (Lactobacillus Casei Shirota strain 3 × 1010 colony forming units (CFU) or a placebo drink over eight weeks. Portable biofeedback devices were used to measure the electroencephalography, heart rate, and electrodermal responses along with cognitive tests at the baseline, week 4, and week 8. Data were statistically analyzed using mixed factorial ANOVA and results revealed that there is no significant difference between the probiotic and placebo groups for heart rate (61.90 bpm ± 5.84 vs. 67.67 bpm ± 8.42, p = 0.09) and electrodermal responses (0.27 µS ± 0.19 vs. 0.41 µS ± 0.12, p = 0.07) after eight weeks. Similarly, brain waves showed no significant changes during the study period except for the theta wave and delta wave at week 4 (p < 0.05). The cognitive test reaction time (digit vigilance test) showed significant improvement in the probiotic group compared to the placebo (p < 0.05). In conclusion, these findings suggest that daily probiotics supplementation may have the potential to modulate the brain waves namely, theta (relaxation) and delta (attention) for better training, brain function, and psychological improvement to exercise. Further research is needed to elucidate the mechanism of current findings.

A Systematic Method for Preprocessing and Analyzing Electrodermal Activity

Electrodermal activity (EDA) is a measure of sympathetic tone using sweat gland activity that has applications in research and clinical medicine. We previously identified never-before-seen statistical structure in EDA. However, there is no systematic method to preprocess and analyze EDA data to capture such statistical structure. Therefore, in this study, we analyzed the data of two healthy volunteers while awake and at rest. We used a systematic process that takes advantage of the tail behavior of various statistical distributions to ensure capturing the point process structure in EDA. We verified the presence of this temporal structure in a new dataset of subjects. Our results demonstrate for the first time that point process structure of EDA pulses can be identified across multiple datasets using a systematic method that is still rooted in the underlying physiology.

Simultaneous measurement of electrodermal activity components correlated with age-related differences

Electrodermal activity (EDA) measurements are influenced by various factors. Age-related psychological and physiological changes may be considered as one of the possible factors which may influence EDA measurements. In order to properly investigate the effects of such factors on EDA, techniques of precisely and simultaneously recording more than one EDA parameter are recommended. This study aims to explore the impact of age-related differences on EDA components through employing a new measuring technique, which is composed of a small front-end electronic box, DAQ card, and a laptop running LabVIEW software. It is dependent on the simultaneous recording of three EDA parameters: skin conductance (SC), skin potential (SP), and skin susceptance (SS) at the same skin site. EDA components as results of breathing, mathematical tasks, and image stimuli were recorded from 60 healthy participants simultaneously at the same skin site. They were categorized by age into young adults (ages 18-25), middle-aged adults (ages 30-40), and old adults (ages 50-70) years. It was found that skin potential responses (SPRs), and skin conductance level (SCL) (p < 0.001), were significantly decreased due to aging, but changes in other EDA parameters were nonsignificant (p > 0.05). Moreover, both tonic and phasic SS were the least affected and found to be more robust than SC and SP with respect to aging. The study suggests that it is important to take age into account in research studies where the mean aim of the study is to compare EDA parameters; however, in the meantime, the results from our small number and specific study population cannot be generalized to clinical applications.

Skin Conductance Response to Gradual-Increasing Experimental Pain

Patient self-reporting of pain is not always possible, in those cases automated objective pain assessment could lead to reliable pain assessment. In this context, physiological measurements have been studied and one of the promising signals is skin conductance (SC). In this study, 1Hz SC signal acquisition is performed while gradually increasing heat and electrical pain stimuli are induced. Three labeled study periods are defined based on pain stimuli presence, self-reported pain threshold and pain tolerance. Different classification and regression models are compared, together with selected SC features. The model performances are evaluated using c-index. Results show good predictability, especially for the slow tonic component decomposed from the SC signal.

The influence of mindfulness meditation on inattention and physiological markers of stress on students with learning disabilities and/or attention deficit hyperactivity disorder

BACKGROUND

Over recent decades, the number of students diagnosed with learning disabilities and/or attention deficit hyperactivity disorders has substantially increased. These students face various challenges and experience stress when receiving higher education.

AIMS

The purpose of this study was to compare two non-pharmacological interventions: mindfulness and device-guided slow breathing, with a control group.

METHODS

Seventy-three students (age = 25.76, std. dev = 3.10) with attention problems and/or learning disabilities were randomly assigned to three groups: mindfulness meditation, device guided breathing practice and waiting-list control. Before and after the intervention physiological and psychological measures were collected.

RESULTS

Our results show that only mindfulness practice improved awareness of the present moment and decreased hyperactivity and inattention. Furthermore, both mindfulness and practice with device-guided breathing were associated with stress reduction, as shown by an increase in the galvanic skin response only in the control group.

CONCLUSIONS

Implementation of the study results may lead to an advance in treating attention deficit disorders and learning disabilities, especially among higher education students.

Prediction of Self-Perceived Stress and Arousal Based on Electrodermal Activity

Electrodermal activity (EDA) reflects the functions of autonomic nervous system and is often used in evaluation of mental states, e.g. short- and long-term stress. In this study, test subjects were exposed to a 3-phase adapted MIST test (relaxation, arousal, stress) during which EDA was recorded, and the self-perceived stress and arousal were assessed. The objective of the study was to evaluate the feasibility of EDA features to predict the MIST test phases and self-perceived stress and arousal. With EDA features, the test phases were classified with accuracy of 94.1%, and the self-perceived stress and arousal were classified with accuracy of 60.5-72.2%. Results are promising for the use of EDA for long-term assessment of self-perceived stress and arousal during work.

Early prevention of diabetes microvascular complications in people with hyperglycaemia in Europe. ePREDICE randomized trial. Study protocol, recruitment and selected baseline data

Objectives

To assess the effects of early management of hyperglycaemia with antidiabetic drugs plus lifestyle intervention compared with lifestyle alone, on microvascular function in adults with pre-diabetes.

Methods

Trial design: International, multicenter, randomised, partially double-blind, placebo-controlled, clinical trial.

Participants

Males and females aged 45–74 years with IFG, IGT or IFG+IGT, recruited from primary care centres in Australia, Austria, Bulgaria, Greece, Kuwait, Poland, Serbia, Spain and Turkey.

Intervention

Participants were randomized to placebo; metformin 1.700 mg/day; linagliptin 5 mg/day or fixed-dose combination of linagliptin/metformin. All patients were enrolled in a lifestyle intervention program (diet and physical activity). Drug intervention will last 2 years. Primary Outcome: composite end-point of diabetic retinopathy estimated by the Early Treatment Diabetic Retinopathy Study Score, urinary albumin to creatinine ratio, and skin conductance in feet estimated by the sudomotor index. Secondary outcomes in a subsample include insulin sensitivity, beta-cell function, biomarkers of inflammation and fatty liver disease, quality of life, cognitive function, depressive symptoms and endothelial function.

Results

One thousand three hundred ninety one individuals with hyperglycaemia were assessed for eligibility, 424 excluded after screening, 967 allocated to placebo, metformin, linagliptin or to fixed-dose combination of metformin + linagliptin. A total of 809 people (91.1%) accepted and initiated the assigned treatment. Study sample after randomization was well balanced among the four groups. No statistical differences for the main risk factors analysed were observed between those accepting or rejecting treatment initiation. At baseline prevalence of diabetic retinopathy was 4.2%, severe neuropathy 5.3% and nephropathy 5.7%.

Conclusions

ePREDICE is the first -randomized clinical trial with the aim to assess effects of different interventions (lifestyle and pharmacological) on microvascular function in people with pre-diabetes. The trial will provide novel data on lifestyle modification combined with glucose lowering drugs for the prevention of early microvascular complications and diabetes.

Registration

- ClinicalTrials.Gov Identifier: NCT03222765

- EUDRACT Registry Number: 2013-000418-39

[Emotion Recognition Based on Multiple Physiological Signals]

Emotion is a series of reactions triggered by a specific object or situation that affects a person's physiological state and can, therefore, be identified by physiological signals. This paper proposes an emotion recognition model. Extracted the features of physiological signals such as photoplethysmography, galvanic skin response, respiration amplitude, and skin temperature. The SVM-RFE-CBR(Recursive Feature Elimination-Correlation Bias Reduction-Support Vector Machine) algorithm was performed to select features and support vector machines for classification. Finally, the model was implemented on the DEAP dataset for an emotion recognition experiment. In the rating scale of valence, arousal, and dominance, the accuracy rates of 73.5%, 81.3%, and 76.1% were obtained respectively. The result shows that emotional recognition can be effectively performed by combining a variety of physiological signals.

Wireless Sensors System for Stress Detection by Means of ECG and EDA Acquisition

This paper describes the design of a two channels electrodermal activity (EDA) sensor and two channels electrocardiogram (ECG) sensor. The EDA sensors acquire data on the hands and transmit them to the ECG sensor with wireless WiFi communication for increased wearability. The sensors system acquires two EDA channels to improve the removal of motion artifacts that take place if EDA is measured on individuals who need to move their hands in their activities. The ECG channels are acquired on the chest and the ECG sensor is responsible for aligning the two ECG traces with the received packets from EDA sensors; the ECG sensor sends via WiFi the aligned packets to a laptop for real time plot and data storage. The metrological characterization showed high-level performances in terms of linearity and jitter; the delays introduced by the wireless transmission from EDA to ECG sensor have been proved to be negligible for the present application.

Classification of Perceived Human Stress using Physiological Signals

In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals. These include electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG). We conducted experiments consisting of steps including data acquisition, feature extraction, and perceived human stress classification. The physiological data of 28 participants are acquired in an open eye condition for a duration of three minutes. Four different features are extracted in time domain from EEG, GSR and PPG signals and classification is performed using multiple classifiers including support vector machine, the Naive Bayes, and multi-layer perceptron (MLP). The best classification accuracy of 75% is achieved by using MLP classifier. Our experimental results have shown that our proposed scheme outperforms existing perceived stress classification methods, where no stress inducers are used.

Visuocortical tuning to a threat-related feature persists after extinction and consolidation of conditioned fear

Neurons in the visual cortex sharpen their orientation tuning as humans learn aversive contingencies. A stimulus orientation (CS+) that reliably predicts an aversive noise (unconditioned stimulus: US) is selectively enhanced in lower-tier visual cortex, while similar unpaired orientations (CS-) are inhibited. Here, we examine in male volunteers how sharpened visual processing is affected by fear extinction learning (where no US is presented), and how fear and extinction memory undergo consolidation one day after the original learning episode. Using steady-state visually evoked potentials from electroencephalography in a fear generalization task, we found that extinction learning prompted rapid changes in orientation tuning: Both conditioned visuocortical and skin conductance responses to the CS+ were strongly reduced. Next-day re-testing (delayed recall) revealed a brief but precise return-of-tuning to the CS+ in visual cortex accompanied by a brief, more generalized return-of-fear in skin conductance. Explorative analyses also showed persistent tuning to the threat cue in higher visual areas, 24 h after successful extinction, outlasting peripheral responding. Together, experience-based changes in the sensitivity of visual neurons show response patterns consistent with memory consolidation and spontaneous recovery, the hallmarks of long-term neural plasticity.

Wearable Sensor System to Monitor Physical Activity and the Physiological Effects of Heat Exposure

Mobile health monitoring via non-invasive wearable sensors is poised to advance telehealth for older adults and other vulnerable populations. Extreme heat and other environmental conditions raise serious health challenges that warrant monitoring of real-time physiological data as people go about their normal activities. Mobile systems could be beneficial for many communities, including elite athletes, military special forces, and at-home geriatric monitoring. While some commercial monitors exist, they are bulky, require reconfiguration, and do not fit seamlessly as a simple wearable device. We designed, prototyped and tested an integrated sensor platform that records heart rate, oxygen saturation, physical activity levels, skin temperature, and galvanic skin response. The device uses a small microcontroller to integrate the measurements and store data directly on the device for up to 48+ h. continuously. The device was compared to clinical standards for calibration and performance benchmarking. We found that our system compared favorably with clinical measures, such as fingertip pulse oximetry and infrared thermometry, with high accuracy and correlation. Our novel platform would facilitate an individualized approach to care, particularly those whose access to healthcare facilities is limited. The platform also can be used as a research tool to study physiological responses to a variety of environmental conditions, such as extreme heat, and can be customized to incorporate new sensors to explore other lines of inquiry.

Complexity-based decoding of brain-skin relation in response to olfactory stimuli

BACKGROUND AND OBJECTIVE

Human body is covered with skin in different parts. In fact, skin reacts to different changes around human. For instance, when the surrounding temperature changes, human skin will react differently. It is known that the activity of skin is regulated by human brain. In this research, for the first time we investigate the relation between the activities of human skin and brain by mathematical analysis of Galvanic Skin Response (GSR) and Electroencephalography (EEG) signals.

METHOD

For this purpose, we employ fractal theory and analyze the variations of fractal dimension of GSR and EEG signals when subjects are exposed to different olfactory stimuli in the form of pleasant odors.

RESULTS

Based on the obtained results, the complexity of GSR signal changes with the complexity of EEG signal in case of different stimuli, where by increasing the molecular complexity of olfactory stimuli, the complexity of EEG and GSR signals increases. The results of statistical analysis showed the significant effect of stimulation on variations of complexity of GSR signal. In addition, based on effect size analysis, fourth odor with greatest molecular complexity had the greatest effect on variations of complexity of EEG and GSR signals.

CONCLUSION

Therefore, it can be said that human skin reaction changes with the variations in the activity of human brain. The result of analysis in this research can be further used to make a model between the activities of human skin and brain that will enable us to predict skin reaction to different stimuli.

Recognition of Negative Emotion using Long Short-Term Memory with Bio-Signal Feature Compression

Negative emotion is one reason why stress causes negative feedback. Therefore, many studies are being done to recognize negative emotions. However, emotion is difficult to classify because it is subjective and difficult to quantify. Moreover, emotion changes over time and is affected by mood. Therefore, we measured electrocardiogram (ECG), skin temperature (ST), and galvanic skin response (GSR) to detect objective indicators. We also compressed the features associated with emotion using a stacked auto-encoder (SAE). Finally, the compressed features and time information were used in training through long short-term memory (LSTM). As a result, the proposed LSTM used with the feature compression model showed the highest accuracy (99.4%) for recognizing negative emotions. The results of the suggested model were 11.3% higher than with a neural network (NN) and 5.6% higher than with SAE.

Tonic and Phasic Decomposition of Skin Conductance Data: A Generalized-Cross-Validation-Based Block Coordinate Descent Approach

Salty sweat secretions in the epidermis change the skin's electrical activity resulting in the measured skin conductance signal. While the relatively fast variation of skin conductance (i.e. phasic component) reflects sympathetic nervous system activity, the slow variation (i. e. tonic component) is related to thermoregulation and general arousal. To better understand the neural information encoded in a skin conductance signal, it is necessary to decompose it into its constituent components. We model the fast variations using a second order differential equation incorporating a sparse impulsive input to the model. Furthermore, we model the tonic component with several cubic basis spline functions. Finally, we develop a block coordinate descent approach for skin conductance signal decomposition by employing generalized-cross-validation for balancing between smoothness of the tonic component, the sparsity of the neural stimuli, and residual error. We analyze experimental and simulated data to validate the performance of the proposed approach. We successfully illustrate its ability to recover the neural stimuli, the underlying physiological system parameters, and both tonic and phasic components. In summary, we develop a novel approach for decomposition of phasic and tonic components of skin conductance signal using a generalized-cross-validation-based block coordinate descent approach. Recovering the underlying neural stimuli and the tonic component accurately could potentially improve cognitive-stress-related arousal states estimation for better stress regulation in mental health disorders.

Detection of Stressful Situations Using GSR While Driving a BCI-controlled Wheelchair

This paper analyzes the galvanic skin response (GSR) recorded from healthy and motor disabled people while steering a robotic wheelchair (RobChair ISR-UC prototype), to infer whether GSR can help in the recognition of stressful situations. Seven healthy individuals and six individuals with motor disabilities were asked to drive the RobChair by means of a brain-computer interface in indoor office environments, including complex scenarios such as passing narrow doors, avoiding obstacles, and with situations of unexpected trajectories of the wheelchair (controlled by an operator without users knowledge). All these driving situations can trigger emotional arousals such as anxiety and stress. A method called feature-based peak detection (FBPD) was proposed for automatic detection of skin conductance response (SCR) which proved to be very effective compared to the state-of-the-art methods. We found that SCR was elicited in 100% of the occurrences of collisions (lateral scrapings) and 94% of unexpected trajectories.

State-Space Modeling and Fuzzy Feedback Control of Cognitive Stress

"Distress" or a substantial amount of stress may decrease brain functionality and cause neurological disorders. On the other hand, very low cognitive arousal may affect one's concentration and awareness. Data collected using wrist-worn wearable devices, in particular, skin conductance data, could be used to look into one's cognitive-stress-related arousal. Our goal here is to present excitatory and inhibitory wearable machine-interface (WMI) architectures to control one's cognitive-stress-related arousal state. We first present a model for skin conductance response events as a function of environmental stimuli associated with cognitive stress and relaxation. Then, we perform Bayesian filtering to estimate the hidden cognitive-stress-related arousal state. We finally close the loop using fuzzy control. In particular, we design two classes of controllers for our WMI architectures: (1) an inhibitory controller for reducing arousal and (2) an excitatory controller for increasing arousal. Our results illustrate that our simulated skin conductance responses are in agreement with experimental data. Moreover, we illustrate that our fuzzy control can successfully have both inhibitory and excitatory effects and regulate one's cognitive stress. In conclusion, in a simulation study based on experimental data, we have illustrated the feasibility of designing both excitatory and inhibitory WMI architectures. Since wearable devices can be used conveniently in one's daily life, the WMI architectures have a great potential to regulate one's cognitive stress seamlessly in real-world situations.

The Role of Haptic Stimuli on Affective Reading: a Pilot Study

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.

Combining Electrodermal Activity and Speech Analysis towards a more Accurate Emotion Recognition System

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.

A Comparative Study of Stress and Anxiety Estimation in Ecological Settings Using a Smart-shirt and a Smart-bracelet

In recent years, consumer wearable devices focused on health assessment have gained popularity. Of these devices, a large number target monitoring heart rate; a few among them include additional biometrics such as breathing rate, galvanic skin response, and skin temperature. Heart rate, and more specifically, heart rate variability (HRV) measures have proven useful in monitoring user psychological states, such as mental workload, stress and anxiety. Most studies, however, have been conducted in controlled laboratory environments with artificially-induced psychological responses. While these conditions assure high quality in the collected data, the amount of data are limited and the generalization of the findings to more ecologically-appropriate settings remains unknown. To this end, in this paper we compare the accuracy of two wearable devices, namely a smart-shirt measuring electrocardiograms and a smart-bracelet measuring photoplethysmograms. Several HRV features are extracted and tested as correlates of stress and anxiety. Data were collected from 196 participants during their normal work shifts for a period of 10 weeks. The complementarity of the two devices is also explored and the advantages of each method are discussed.

Decreasing motion sickness by mixing different techniques

We investigated the effectiveness of galvanic cutaneous stimulation (GCS) and auditory stimulation (AS) together and separately in mitigating motion sickness (MS). Forty-eight drivers (twenty-two men; mean age = 21.58 years) participated in a driving simulation experiment. We compared the total scores of the Simulator Sickness Questionnaire (SSQ) across four different stimulation conditions (GCS, AS, Mixed GCS-AS and no stimulation as a baseline condition). We provided evidence that mixing techniques mitigates MS owing to an improvement in body balance; furthermore, mixing techniques improves driving behavior more effectively than GCS and AS in isolation. We encourage the use of the two techniques together to decrease MS.

Renewal of fear and avoidance in humans to escalating threat: Implications for translational research on anxiety disorders

Exposure-based treatment for threat avoidance in anxiety disorders often results in fear renewal. However, little is known about renewal of avoidance. This multimodal laboratory-based treatment study used an ABA renewal design and an approach-avoidance (AP-AV) task to examine renewal of fear/threat and avoidance in twenty adults. In Context A, 9 visual cues paired with increases in probabilistic money loss (escalating threats) produced increases in ratings of feeling threatened and loss expectancies and skin-conductance responses (SCR). During the AP-AV task, a monetary reinforcer was available concurrently with threats. Approach produced the reinforcer or probabilistic loss, while avoidance prevented loss and forfeited reinforcement. Escalating threat produced increasing avoidance and ratings. In Context B with Pavlovian extinction, threats signaled no money loss and SCR declined. During the AP-AV task, avoidance and ratings also declined. In a return to Context A with Pavlovian threat extinction in effect during the AP-AV task, renewal was observed. Escalating threat was associated with increasing ratings and avoidance in most participants. SCR did not show renewal. These are the first translational findings to highlight renewal of avoidance in humans. Further research should identify individual difference variables and altered neural mechanisms that may confer increased risk of avoidance renewal.

Mild Dehydration Identification Using Machine Learning to Assess Autonomic Responses to Cognitive Stress

The feasibility of detecting mild dehydration by using autonomic responses to cognitive stress was studied. To induce cognitive stress, subjects (n = 17) performed the Stroop task, which comprised four minutes of rest and four minutes of test. Nine indices of autonomic control based on electrodermal activity (EDA) and pulse rate variability (PRV) were obtained during both the rest and test stages of the Stroop task. Measurements were taken on three consecutive days in which subjects were "wet" (not dehydrated) and "dry" (experiencing mild dehydration caused by fluid restriction). Nine approaches were tested for classification of "wet" and "dry" conditions: (1) linear (LDA) and (2) quadratic discriminant analysis (QDA), (3) logistic regression, (4) support vector machines (SVM) with cubic, (5) fine Gaussian kernel, (6) medium Gaussian kernel, (7) a k-nearest neighbor (KNN) classifier, (8) decision trees, and (9) subspace ensemble of KNN classifiers (SE-KNN). The classification models were tested for all possible combinations of the nine indices of autonomic nervous system control, and their performance was assessed by using leave-one-subject-out cross-validation. An overall accuracy of mild dehydration detection was 91.2% when using the cubic SE-KNN and indices obtained only at rest, and the accuracy was 91.2% when using the cubic SVM classifiers and indices obtained only at test. Accuracy was 86.8% when rest-to-test increments in the autonomic indices were used along with the KNN and QDA classifiers. In summary, measures of autonomic function based on EDA and PRV are suitable for detecting mild dehydration and could potentially be used for the noninvasive testing of dehydration.

Socially transmitted placebo effects

Medical treatments typically occur in the context of a social interaction between healthcare providers and patients. Although decades of research have demonstrated that patients' expectations can dramatically affect treatment outcomes, less is known about the influence of providers' expectations. Here we systematically manipulated providers' expectations in a simulated clinical interaction involving administration of thermal pain and found that patients' subjective experiences of pain were directly modulated by providers' expectations of treatment success, as reflected in the patients' subjective ratings, skin conductance responses and facial expression behaviours. The belief manipulation also affected patients' perceptions of providers' empathy during the pain procedure and manifested as subtle changes in providers' facial expression behaviours during the clinical interaction. Importantly, these findings were replicated in two more independent samples. Together, our results provide evidence of a socially transmitted placebo effect, highlighting how healthcare providers' behaviour and cognitive mindsets can affect clinical interactions.

Fear conditioning and extinction in anxious youth, offspring at-risk for anxiety and healthy comparisons: An fMRI study

Dysfunctions in fronto-amygdala circuitry have been linked to anxiety. Questions remain regarding the impact of familial-risk and ongoing anxiety on such circuitry function, especially in youth. Using fMRI fear conditioning and extinction paradigms, we examined these relationships in 10-17 year-olds: 22 youth with an anxiety disorder, 22 healthy youth born to parents with past or current anxiety disorders (at risk), and 32 healthy comparisons. Skin conductance responses and subjective fear ratings were also assessed. During conditioning, healthy comparisons showed differential activation (CS + >CS-) in regions of the fronto-amygdala circuitry. In comparison, the at-risk group showed greater activation to the safety cue (CS - >CS+) in the amygdala and dorsolateral prefrontal cortex. Failure to show differential fear conditioning in the fronto-amygdala circuitry and impairment in extinction learning was specific to anxious youth. These findings expand our ability to track anxiety-related alterations and potential resilience markers to anxiety.

Continuous Pain Intensity Estimation from Autonomic Signals with Recurrent Neural Networks

Pain is usually measured by patient's self-report, which requires patient collaboration. Hence, the development of an objective automatic pain detection method would be useful in many clinical applications and patient populations. Previous studies have explored the feasibility of using physiological autonomic signals to detect the presence of pain. In this study, we focused on continuously estimating experimental heat pain intensity with high temporal resolution from autonomic signals. Specifically, we employed skin conductance deconvolution and point process heart rate variability analysis to continuously evaluate time-varying autonomic parameters, and presented a regression algorithm based on recurrent neural networks.

A State-Space Approach for Detecting Stress from Electrodermal Activity

The human body responds to neurocognitive stress in multiple ways through its autonomic nervous system. Increases in heart rate, salivary cortisol and skin conductance level are often observed accompanying high levels of stress. Stress can also take on different forms including emotional, cognitive and motivational. While a precise definition for stress is lacking, a pertinent issue is to quantify the state of psychological stress manifested in the nervous system. State-space models have previously been applied to estimate an unobserved neural state (e.g. learning, consciousness) from physiological signal measurements and data collected during behavioral experiments. In this paper, we relate stress to the probability that a phasic driver impulse occurs in skin conductance signals. We apply state-space modeling to extracted binary measures to continuously track a stress level across episodes of cognitive and emotional stress as well as relaxation. Results demonstrate a promising approach for tracking stress through wearable devices.

A New Sympathovagal Balance Index from Electrodermal Activity and Instantaneous Vagal Dynamics: A Preliminary Cold Pressor Study

Sympathovagal balance, an autonomic index resulting from the sympathetic and parasympathetic influences on cardiovascular control, has been extensively used in the research practice. The current assessment is based on analyzing Heart Rate Variability (HRV) series in the frequency domain by regarding the ratio between the low and high frequency components (LF/HF). Nevertheless, LF and HF powers are known to be both influenced by vagal activity which strongly bias the accuracy of this method. To this extent, in this study we combine time-varying estimates from electrodermal activity (EDA) and HRV to propose a novel index of sympathovagal balance. Particularly, sympathetic activity is estimated from the EDA power calculated within the 0.045-0.25Hz bandwidth $(EDA_{Symp})$, whereas parasympathetic dynamics is measured instantaneously through a point-process modeling framework devised for heartbeat dynamics $(HF_{pp})$. We test our new index $SV = EDA_{Symp/HF_{pp}}$ on data gathered from 22 healthy subjects (7 females and 15 males) undergoing a 3 minutes gold standard protocol for sympathetic elicitation as the cold-pressor test (CPT). Results show that the activation of the proposed sympathovagal tone is consistent with CPT elicitation and is associated with a significantly higher statistical discriminant power than the standard LF/HF ratio, also revealing different dynamics between female and male subjects.

Fairness norm violations in anti-social psychopathic offenders in a repeated trust game

Psychopathic offenders have a high propensity to violate social norms, as indicated for instance by their widespread lying and cheating behaviour. The reasons for their norm violations are not well understood, though, as they are able to recognise norms in a given situation and also punish norm violators. In this study, we investigated whether psychopathic offenders would violate fairness norms during a repeated trust game because of increased profit-maximising concerns. We measured back-transfer decisions in the repeated trust game, and affective arousal by means of skin conductance responses (SCR) in violent offenders with varying degrees of psychopathy, and non-offenders with low-trait psychopathy. Psychopathy in offenders was measured with the Psychopathy Checklist-Revised (PCL-R). In the task, a participant and an interaction partner entrusted each other money for multiple rounds with the goal to earn as much money as possible. Fairness norm violations were positively associated with Factor 2 scores (the lifestyle/anti-social psychopathy subscale) of the PCL-R, but this was not accompanied by clear profit-maximising behaviour. In addition, anticipatory arousal to self-advantageous decisions was higher in all offenders, independent of their degree of psychopathy, compared with non-offenders. The results of our study widen our understanding of social decision-making in psychopathy. They also suggest treatment possibilities in offenders scoring high on Factor 2, targeting empathic concern and related prosocial intentions to overcome norm-violating behaviour.

An Exploration of Machine Learning Methods for Robust Boredom Classification Using EEG and GSR Data

In recent years, affective computing has been actively researched to provide a higher level of emotion-awareness. Numerous studies have been conducted to detect the user's emotions from physiological data. Among a myriad of target emotions, boredom, in particular, has been suggested to cause not only medical issues but also challenges in various facets of daily life. However, to the best of our knowledge, no previous studies have used electroencephalography (EEG) and galvanic skin response (GSR) together for boredom classification, although these data have potential features for emotion classification. To investigate the combined effect of these features on boredom classification, we collected EEG and GSR data from 28 participants using off-the-shelf sensors. During data acquisition, we used a set of stimuli comprising a video clip designed to elicit boredom and two other video clips of entertaining content. The collected samples were labeled based on the participants' questionnaire-based testimonies on experienced boredom levels. Using the collected data, we initially trained 30 models with 19 machine learning algorithms and selected the top three candidate classifiers. After tuning the hyperparameters, we validated the final models through 1000 iterations of 10-fold cross validation to increase the robustness of the test results. Our results indicated that a Multilayer Perceptron model performed the best with a mean accuracy of 79.98% (AUC: 0.781). It also revealed the correlation between boredom and the combined features of EEG and GSR. These results can be useful for building accurate affective computing systems and understanding the physiological properties of boredom.

Sharing the filmic experience - The physiology of socio-emotional processes in the cinema

As we identify with characters on screen, we simulate their emotions and thoughts. This is accompanied by physiological changes such as galvanic skin response (GSR), an indicator for emotional arousal, and respiratory sinus arrhythmia (RSA), referring to vagal activity. We investigated whether the presence of a cinema audience affects these psychophysiological processes. The study was conducted in a real cinema in Berlin. Participants came twice to watch previously rated emotional film scenes eliciting amusement, anger, tenderness or fear. Once they watched the scenes alone, once in a group. We tested whether the vagal modulation in response to the mere presence of others influences explicit (reported) and implicit markers (RSA, heart rate (HR) and GSR) of emotional processes in function of solitary or collective enjoyment of movie scenes. On the physiological level, we found a mediating effect of vagal flexibility to the mere presence of others. Individuals showing a high baseline difference (alone vs. social) prior to the presentation of film, maintained higher RSA in the alone compared to the social condition. The opposite pattern emerged for low baseline difference individuals. Emotional arousal as reflected in GSR was significantly more pronounced during scenes eliciting anger independent of the social condition. On the behavioural level, we found evidence for emotion-specific effects on reported empathy, emotional intensity and Theory of Mind. Furthermore, people who decrease their RSA in response to others' company are those who felt themselves more empathically engaged with the characters. Our data speaks in favour of a specific role of vagal regulation in response to the mere presence of others in terms of explicit empathic engagement with characters during shared filmic experience.

A Bayesian Filtering Approach for Tracking Arousal From Binary and Continuous Skin Conductance Features

OBJECTIVE

Neuroanatomical structures within the cortical and sub-cortical brain regions process emotion and cause subsequent variations in signals such as skin conductance and electrocardiography. The signals often encode information in their continuous-valued amplitudes or waves as well as in their underlying impulsive events. We propose to track psychological arousal from this hybrid source of skin conductance information.

METHODS

We present a point process state-space method in tandem with Bayesian filtering for determining a continuous-valued arousal state from skin conductance measurements. To perform state estimation, we relate arousal to binary- and continuous-valued observations derived from the phasic and tonic parts of a skin conductance signal, and recover model parameters using expectation-maximization. We evaluate our model on both synthetic and two different experimental data sets. Stress was artificially induced in the first experimental data set and the second comprised of a fear conditioning experiment.

RESULTS

Results on the first data set indicate high levels of arousal during exposure to cognitive stress and low arousal during relaxation. Plausible results are also obtained in the fear conditioning data set consistent with previous skin conductance studies in similar experimental contexts.

CONCLUSION

The state-space approach-which does not rely on external classification labels-is able to continuously track an arousal level from skin conductance features.

SIGNIFICANCE

The method is a promising arousal estimation scheme utilizing only skin conductance. The approach could find applications in wearable monitoring and the study of neuropsychiatric conditions such as post-traumatic stress disorder.

Skin Conductance and RR Interval for Regulated Discrete Physiological Stimuli: A Two Prong Strategy to Detect Sympathetic Activation

Background Several studies have found skin conductance a good indicator for detection of sympathetic response. But, valid and reliable tool for detection of sympathetic outflow in health and disease is still a quest. Thereby, comparison of superficial and, at core sympathetic effluence induced by deliberately supplied discrete external stimuli has been attempted in this study. Objective To assess the degree of sympathetic outflow for discrete cognitive and physical stimuli through perturbations in skin conductance and variations in heart rate in healthy adults. Method Quantitative and cross-sectional study was performed in 104 healthy subjects following random sampling method. Induction of sympathetic activity was realized by providing separate time bound cognitive exercises intervened with change in posture. Recordings to detect sympathetic responses at rest and, for supplied stimuli were made by electrocardiogram and galvanic skin response. Result Cognitive performance and postural change shifts baseline effluence and increases the sympathetic outflow significantly (p=0.000). There occurs no detectable rise in sympathetic effluence at the core (p=0.362) but, eventuate significantly appreciable sympathetic outflow to sweat glands in skin (p=0.000), when compared cognitive versus physical stimuli. Conclusion Sympathetic outflow induced by cognitive challenge and physical change in posture is readily assessable through sympathetic skin response yet core sympathetic effluence for latter stimuli is steady and unwavering. Differential effluence for sympathetic response called upon by discrete stimuli is operational for maintenance of steady state in healthy subjects.

A Point Process Characterization Of Electrodermal Activity

Electrodermal activity (EDA) is a measure of sympathetic activity using skin conductance that has applications in research and in clinical medicine. However, current EDA analysis does not have physiologically-based statistical models that use stochastic structure to provide nuanced insight into autonomic dynamics. Therefore, in this study, we analyzed the data of two healthy volunteers under controlled propofol sedation. We identified a novel statistical model for EDA and used a point process framework to track instantaneous dynamics. Our results demonstrate for the first time that point process models rooted in physiology and built upon inherent statistical structure of EDA pulses have the potential to accurately track instantaneous dynamics in sympathetic tone.