Fear conditioning is preserved in very preterm-born young adults despite increased anxiety levels

Very preterm birth is associated with an increased risk for anxiety disorders. Abnormal brain development may result in disordered fear learning processes, which may be exacerbated by environmental risk factors and persist in adulthood. We tested the hypotheses that very preterm-born young adults displayed higher levels of fear conditioning, less differentiation between threat (CS+) and safety (CS-) signals, and stronger resistance to extinction relative to term-born controls. A group of 37 very preterm-born young adults and 31 age- and sex-matched term-born controls performed a differential fear conditioning paradigm on two consecutive days. Acquisition and extinction training were performed on day 1. Recall and reinstatement were tested on day 2. Preterm-born participants showed significantly higher levels of anxiety in the Depression-Anxiety-Stress-Scale-21 questionnaire. The fear conditioning outcome measures, skin conductance response amplitudes and anxiety ratings, were overall higher in the preterm-born group compared to controls. Awareness of CS-US contingencies was mildly reduced in preterms. Acquisition, extinction, recall and reinstatement of differential conditioned fear responses (CS+  > CS-), however, were not significantly different between the groups. There were no significant group by stimulus type interactions. The finding of largely preserved associative fear learning in very preterm-born young adults was unexpected and needs to be confirmed in future studies.

Study of sympathetic skin response in patients with COVID-19 infection

OBJECTIVES

Many articles hypothesized the potential role of autonomic nervous system in the pathogenesis and outcome of COVID-19 infection. Several studies reported both central and peripheral nervous system involvement in COVID-19 as well. Up to our knowledge, there is no study evaluating whether this virus could invade the autonomic nervous system affecting its function adversely. Sympathetic skin response (SSR) has long been used as a method of evaluating the autonomic nervous system. Regarding the importance of the autonomic nervous system in hemostasis and wide consequences of COVID-19 infection, we designed this study to evaluate the autonomic nervous system function in patients recovered from COVID-19 compared with normal population who are not yet infected by this virus by the means of SSR.

METHODS

This case-control study included 70 patients surviving COVID-19 who met the inclusion and exclusion criteria that went under SSR. The data gathered were compared with those without the history of any symptoms attributable to COVID-19 during the pandemic.

RESULTS

There was a correlation between COVID-19 infection and abnormal SSR (p value < 0.0001) with the most effect on the latency prolongation of the action potential recorded from the median nerve at palms (effect size: right: 3.90, left: 3.69). Moreover, the greater severity of the disease correlated with more abnormality of parameters recorded by SSR technique.

CONCLUSIONS

Abnormal SSR parameters could be a good indicator of autonomic nervous system involvement in patients with COVID-19 infection. It might be a predictor of disease severity, clinical outcomes and prognosis as well.

Manipulating expectancy violations to strengthen the efficacy of human fear extinction

Recent theoretical and clinical articles have emphasized a role for expectancy violations in improving the effectiveness of exposure therapy. Expectancy violations are critical to extinction learning and strengthening these violations has been suggested to improve the formation and retention of extinction memories, which should result in lasting symptom reductions after treatment. However, more detailed mechanistic insights in this process are needed to better inform clinical interventions. In two separate fear-conditioning experiments, we investigated whether stronger expectancy violations (Exp1) or fostering awareness of expectancy violations (Exp2) during extinction could reduce the subsequent return of fear. We measured fear potentiated startle (FPS) and skin conductance responses (SCR) as physiological indices of fear, and US expectancy ratings to assess our manipulations. While we successfully created stronger expectancy violations in Exp1, we found no evidence that these stronger violations reduced the return of fear at test. Interestingly, fostering awareness of violations (Exp2) reduced differential SCRs, but not FPS responses. These findings provide novel insights into the effect of US expectancies on fear extinction in the lab, but they also illustrate the complexity of capturing clinically relevant processes of change with fear-conditioning studies.

The effect of flight phase on electrodermal activity and gaze behavior: A simulator study

Current advances in airplane cockpit design and layout are often driven by a need to improve the pilot's awareness of the aircraft's state. This involves an improvement in the flow of information from aircraft to pilot. However, providing the aircraft with information on the pilot's state remains an open challenge. This work takes a first step towards determining the pilot's state based on biosensor data. We conducted a simulator study to record participants' electrodermal activity and gaze behavior, indicating pilot state changes during three distinct flight phases in an instrument failure scenario. The results show a significant difference in these psychophysiological measures between a phase of regular flight, the incident phase, and a phase with an additional troubleshooting task after the failure. The differences in the observed measures suggest great potential for a pilot-aware cockpit that can provide assistance based on the sensed pilot state.

Wrist-Based Electrodermal Activity Monitoring for Stress Detection Using Federated Learning

With the most recent developments in wearable technology, the possibility of continually monitoring stress using various physiological factors has attracted much attention. By reducing the detrimental effects of chronic stress, early diagnosis of stress can enhance healthcare. Machine Learning (ML) models are trained for healthcare systems to track health status using adequate user data. Insufficient data is accessible, however, due to privacy concerns, making it challenging to use Artificial Intelligence (AI) models in the medical industry. This research aims to preserve the privacy of patient data while classifying wearable-based electrodermal activities. We propose a Federated Learning (FL) based approach using a Deep Neural Network (DNN) model. For experimentation, we use the Wearable Stress and Affect Detection (WESAD) dataset, which includes five data states: transient, baseline, stress, amusement, and meditation. We transform this raw dataset into a suitable form for the proposed methodology using the Synthetic Minority Oversampling Technique (SMOTE) and min-max normalization pre-processing methods. In the FL-based technique, the DNN algorithm is trained on the dataset individually after receiving model updates from two clients. To decrease the over-fitting effect, every client analyses the results three times. Accuracies, Precision, Recall, F1-scores, and Area Under the Receiver Operating Curve (AUROC) values are evaluated for each client. The experimental result shows the effectiveness of the federated learning-based technique on a DNN, reaching 86.82% accuracy while also providing privacy to the patient's data. Using the FL-based DNN model over a WESAD dataset improves the detection accuracy compared to the previous studies while also providing the privacy of patient data.

Robustness of Physiological Synchrony in Wearable Electrodermal Activity and Heart Rate as a Measure of Attentional Engagement to Movie Clips

Individuals that pay attention to narrative stimuli show synchronized heart rate (HR) and electrodermal activity (EDA) responses. The degree to which this physiological synchrony occurs is related to attentional engagement. Factors that can influence attention, such as instructions, salience of the narrative stimulus and characteristics of the individual, affect physiological synchrony. The demonstrability of synchrony depends on the amount of data used in the analysis. We investigated how demonstrability of physiological synchrony varies with varying group size and stimulus duration. Thirty participants watched six 10 min movie clips while their HR and EDA were monitored using wearable sensors (Movisens EdaMove 4 and Wahoo Tickr, respectively). We calculated inter-subject correlations as a measure of synchrony. Group size and stimulus duration were varied by using data from subsets of the participants and movie clips in the analysis. We found that for HR, higher synchrony correlated significantly with the number of answers correct for questions about the movie, confirming that physiological synchrony is associated with attention. For both HR and EDA, with increasing amounts of data used, the percentage of participants with significant synchrony increased. Importantly, we found that it did not matter how the amount of data was increased. Increasing the group size or increasing the stimulus duration led to the same results. Initial comparisons with results from other studies suggest that our results do not only apply to our specific set of stimuli and participants. All in all, the current work can act as a guideline for future research, indicating the amount of data minimally needed for robust analysis of synchrony based on inter-subject correlations.

Differentiating between stress- and EPT-induced electrodermal activity during dental examination

Dental pain invokes the sympathetic nervous system, which can be measured by electrodermal activity (EDA). In the dental clinic, accurate quantification of pain is needed because it could enable optimized drug-dose treatments, thereby potentially reducing drug addiction. However, a confounding factor is that during pain there is also lingering residual stress, hence, both contribute to the EDA response. Therefore, we investigated whether EDA can differentiate stress from pain during dental examination. The use of electrical pulp test (EPT) is an ideal approach to tease out the dynamics of stress and mimic pain with lingering residual stress. Once the electrical sensation is felt and reaches a critical current threshold, the subject removes the probe from their tooth, hence, this stage of data represents largely EPT stimulus and the residual stress-induced EDA response is smaller. EPT was performed on necrotic and vital teeth in fifty-one subjects. We defined four different data groups of reactions based on each individual's EPT intensity level expectation based on the visual analog scale (VAS) of their baseline trial, as follows: mild stress, mild stress + EPT, strong stress, and strong stress + EPT. EDA-derived features exhibited significant difference between residual lingering stress + EPT groups and stress groups. We obtained 84.6% accuracy with 76.2% sensitivity and 86.8% specificity with multilayer perceptron in differentiating between pure-stress groups vs. stress + EPT groups. Moreover, EPT induced much greater EDA amplitude and faster response than stress. Our finding suggests that our machine learning approach can discriminate between stress and EPT stimulation in EDA signals.

Systematically investigating the role of context on effect replicability in reinstatement of fear in humans

Context is crucial in guiding behavior in an ever-changing world and contextual information plays a crucial role in associative learning processes. For instance, the return of fear (RoF) after successful extinction, which is used to study the mechanisms underlying relapse phenomena in fear- and stress-related disorders in an experimental model, is known to be context dependent as evident from phenomena such as renewal (contextual change) and reinstatement (re-exposure to an aversive event). Human adaptions of reinstatement paradigms have resulted in mixed findings: CS specific as well as unspecific RoF or unexpected "reinstated" conditioned responding in no reinstatement US control groups. Here, we systematically investigate the role of context (i.e., cue-context compound) on reinstatement-induced RoF in a human differential fear conditioning paradigm using subjective and psychophysiological measures in a large sample (N = 212) including reinstatement and control groups. Overall, response patterns in reinstatement-groups mirrored results from single-cue rodent work. Yet, only generalized, not differential RoF was observed. Remarkably, depending on outcome measure RoF was also observed under identical experimental context conditions without US-re-exposure, underlining effects of contextual change beyond the reinstatement-US and challenging reinstatement research in human subjects and highlight that future reinstatement work should focus on the operationalization of context.

Stress State Classification Based on Deep Neural Network and Electrodermal Activity Modeling

Electrodermal Activity (EDA) has become of great interest in the last several decades, due to the advent of new devices that allow for recording a lot of psychophysiological data for remotely monitoring patients' health. In this work, a novel method of analyzing EDA signals is proposed with the ultimate goal of helping caregivers assess the emotional states of autistic people, such as stress and frustration, which could cause aggression onset. Since many autistic people are non-verbal or suffer from alexithymia, the development of a method able to detect and measure these arousal states could be useful to aid with predicting imminent aggression. Therefore, the main objective of this paper is to classify their emotional states to prevent these crises with proper actions. Several studies were conducted to classify EDA signals, usually employing learning methods, where data augmentation was often performed to countervail the lack of extensive datasets. Differently, in this work, we use a model to generate synthetic data that are employed to train a deep neural network for EDA signal classification. This method is automatic and does not require a separate step for features extraction, as in EDA classification solutions based on machine learning. The network is first trained with synthetic data and then tested on another set of synthetic data, as well as on experimental sequences. In the first case, an accuracy of 96% is reached, which becomes 84% in the second case, thus demonstrating the feasibility of the proposed approach and its high performance.

Driver Attention Assessment Using Physiological Measures from EEG, ECG, and EDA Signals

In this paper, we consider the evaluation of the mental attention state of individuals driving in a simulated environment. We tested a pool of subjects while driving on a highway and trying to overcome various obstacles placed along the course in both manual and autonomous driving scenarios. Most systems described in the literature use cameras to evaluate features such as blink rate and gaze direction. In this study, we instead analyse the subjects' Electrodermal activity (EDA) Skin Potential Response (SPR), their Electrocardiogram (ECG), and their Electroencephalogram (EEG). From these signals we extract a number of physiological measures, including eye blink rate and beta frequency band power from EEG, heart rate from ECG, and SPR features, then investigate their capability to assess the mental state and engagement level of the test subjects. In particular, and as confirmed by statistical tests, the signals reveal that in the manual scenario the subjects experienced a more challenged mental state and paid higher attention to driving tasks compared to the autonomous scenario. A different experiment in which subjects drove in three different setups, i.e., a manual driving scenario and two autonomous driving scenarios characterized by different vehicle settings, confirmed that manual driving is more mentally demanding than autonomous driving. Therefore, we can conclude that the proposed approach is an appropriate way to monitor driver attention.

Explainable Artificial Intelligence (XAI) in Pain Research: Understanding the Role of Electrodermal Activity for Automated Pain Recognition

Artificial intelligence and especially deep learning methods have achieved outstanding results for various applications in the past few years. Pain recognition is one of them, as various models have been proposed to replace the previous gold standard with an automated and objective assessment. While the accuracy of such models could be increased incrementally, the understandability and transparency of these systems have not been the main focus of the research community thus far. Thus, in this work, several outcomes and insights of explainable artificial intelligence applied to the electrodermal activity sensor data of the PainMonit and BioVid Heat Pain Database are presented. For this purpose, the importance of hand-crafted features is evaluated using recursive feature elimination based on impurity scores in Random Forest (RF) models. Additionally, Gradient-weighted class activation mapping is applied to highlight the most impactful features learned by deep learning models. Our studies highlight the following insights: (1) Very simple hand-crafted features can yield comparative performances to deep learning models for pain recognition, especially when properly selected with recursive feature elimination. Thus, the use of complex neural networks should be questioned in pain recognition, especially considering their computational costs; and (2) both traditional feature engineering and deep feature learning approaches rely on simple characteristics of the input time-series data to make their decision in the context of automated pain recognition.

Fear in the mind's eye: the neural correlates of differential fear acquisition to imagined conditioned stimuli

Mental imagery is involved in both the expression and treatment of fear-related disorders such as anxiety and post-traumatic stress disorder. However, the neural correlates associated with the acquisition and generalization of differential fear conditioning to imagined conditioned stimuli are relatively unknown. In this study, healthy human participants (n = 27) acquired differential fear conditioning to imagined conditioned stimuli paired with a physical unconditioned stimulus (i.e. mild shock), as measured via self-reported fear, the skin conductance response and significant right anterior insula (aIn) activation. Multivoxel pattern analysis cross-classification also demonstrated that the pattern of activity in the right aIn during imagery acquisition was quantifiably similar to the pattern produced by standard visual acquisition. Additionally, mental imagery was associated with significant differential fear generalization. Fear conditioning acquired to imagined stimuli generalized to viewing those same stimuli as measured with self-reported fear and right aIn activity, and likewise fear conditioning to visual stimuli was associated with significant generalized differential self-reported fear and right aIn activity when imagining those stimuli. Together, the study provides a novel understanding of the neural mechanisms associated with the acquisition of differential fear conditioning to imagined stimuli and that of the relationship between imagery and emotion more generally.

System and Method for Driver Drowsiness Detection Using Behavioral and Sensor-Based Physiological Measures

The amount of road accidents caused by driver drowsiness is one of the world's major challenges. These accidents lead to numerous fatal and non-fatal injuries which impose substantial financial strain on individuals and governments every year. As a result, it is critical to prevent catastrophic accidents and reduce the financial burden on society caused by driver drowsiness. The research community has primarily focused on two approaches to identify driver drowsiness during the last decade: intrusive and non-intrusive. The intrusive approach includes physiological measures, and the non-intrusive approach includes vehicle-based and behavioral measures. In an intrusive approach, sensors are used to detect driver drowsiness by placing them on the driver's body, whereas in a non-intrusive approach, a camera is used for drowsiness detection by identifying yawning patterns, eyelid movement and head inclination. Noticeably, most research has been conducted in driver drowsiness detection methods using only single measures that failed to produce good outcomes. Furthermore, these measures were only functional in certain conditions. This paper proposes a model that combines the two approaches, non-intrusive and intrusive, to detect driver drowsiness. Behavioral measures as a non-intrusive approach and sensor-based physiological measures as an intrusive approach are combined to detect driver drowsiness. The proposed hybrid model uses AI-based Multi-Task Cascaded Convolutional Neural Networks (MTCNN) as a behavioral measure to recognize the driver's facial features, and the Galvanic Skin Response (GSR) sensor as a physiological measure to collect the skin conductance of the driver that helps to increase the overall accuracy. Furthermore, the model's efficacy has been computed in a simulated environment. The outcome shows that the proposed hybrid model is capable of identifying the transition from awake to a drowsy state in the driver in all conditions with the efficacy of 91%.

Stress Detection Using Frequency Spectrum Analysis of Wrist-Measured Electrodermal Activity

The article deals with the detection of stress using the electrodermal activity (EDA) signal measured at the wrist. We present an approach for feature extraction from EDA. The approach uses frequency spectrum analysis in multiple frequency bands. We evaluate the proposed approach using the 4 Hz EDA signal measured at the wrist in the publicly available Wearable Stress and Affect Detection (WESAD) dataset. Seven existing approaches to stress detection using EDA signals measured by wrist-worn sensors are analysed and the reported results are compared with ours. The proposed approach represents an improvement in accuracy over the other techniques studied. Moreover, we focus on time to detection (TTD) and show that our approach is able to outperform competing techniques, with fewer data points. The proposed feature extraction is computationally inexpensive, thus the presented approach is suitable for use in real-world wearable applications where both short response times and high detection performance are important. We report both binary (stress vs. no stress) as well as three-class (baseline/stress/amusement) results.

Feasibility of Electrodermal Activity and Photoplethysmography Data Acquisition at the Foot Using a Sock Form Factor

Wearable devices have been shown to play an important role in disease prevention and health management, through the multimodal acquisition of peripheral biosignals. However, many of these wearables are exposed, limiting their long-term acceptability by some user groups. To overcome this, a wearable smart sock integrating a PPG sensor and an EDA sensor with textile electrodes was developed. Using the smart sock, EDA and PPG measurements at the foot/ankle were performed in test populations of 19 and 15 subjects, respectively. Both measurements were validated by simultaneously recording the same signals with a standard device at the hand. For the EDA measurements, Pearson correlations of up to 0.95 were obtained for the SCL component, and a mean consensus of 69% for peaks detected in the two locations was obtained. As for the PPG measurements, after fine-tuning the automatic detection of systolic peaks, the index finger and ankle, accuracies of 99.46% and 87.85% were obtained, respectively. Moreover, an HR estimation error of 17.40±14.80 Beats-Per-Minute (BPM) was obtained. Overall, the results support the feasibility of this wearable form factor for unobtrusive EDA and PPG monitoring.

Understanding Human Fear Extinction: Insights from Psychophysiology

The study of fear extinction has been driven largely by Pavlovian fear conditioning methods across the translational spectrum. The primary methods used to study these processes in humans have been recordings of skin conductance (historically termed galvanic skin response) and fear-potentiation of the acoustic startle reflex. As outlined in the following chapter, the combined corpus of this work has demonstrated the value of psychophysiology in better understanding the underlying neurobiology of extinction learning in healthy humans as well as those with psychopathologies. In addition, psychophysiological approaches, which allow for the preservation of methods between species, have shown their applicability to the assessment of wide-ranging treatment effects. The chapter concludes with potential trajectories for future study in this area.

Examining Stress and Residual Symptoms in Remitted and Partially Remitted Depression Using a Wearable Electrodermal Activity Device: A Pilot Study

Consistent evidence suggests residual symptoms and stress are the most reliable predictors of relapse in remitted depression. Prevailing methodologies often do not enable continuous real-time sampling of stress. Thus, little is known about day-to-day interactions between residual symptoms and stress in remitted depression. In preparation for a full-scale trial, this study aimed to pilot a wrist-worn wearable electrodermal activity monitor: ADI (Analog Devices, Inc.) Study Watch for assessing interactions between physiological stress and residual depressive symptoms following depression remission. 13 individuals remitted from major depression completed baseline, daily diary, and post-daily diary assessments. Self-reported stress and residual symptoms were measured at baseline and post-daily diary. Diary assessments required participants to wear ADI's Study Watch during waking hours and complete self-report questionnaires every evening over one week. Sleep problems, fatigue, energy loss, and agitation were the most frequently reported residual symptoms. Average skin conductance responses (SCRs) were 16.09 per-hour, with an average of 11.30 hours of wear time per-day. Increased residual symptoms were associated with enhanced self-reported stress on the same day. Increased SCRs on one day predicted increased residual symptoms on the next day. This study showed a wearable electrodermal activity device can be recommended for examining stress as a predictor of remitted depression. This study also provides preliminary work on relationships between residual symptoms and stress in remitted depression. Importantly, significant findings from the small sample of this pilot are preliminary with an aim to follow up with a 3-week full-scale study to draw conclusions about psychological processes explored. Clinical and Translational Impact Statemen-ADI's wearable electrodermal activity device enables a continuous measure of physiological stress for identifying its interactions with residual depressive symptoms following remission. This novel procedure is promising for future studies.

A data multiverse analysis investigating non-model based SCR quantification approaches

Electrodermal signals are commonly used outcome measures in research on arousal, emotion, and habituation. Recently, we reported on heterogeneity in skin conductance response quantification approaches and its impact on replicability. Here we provide complementary work focusing on within-approach heterogeneity of specifications for skin conductance response quantification. We focus on heterogeneity within the baseline-correction approach (BLC) which appeared as particularly heterogeneous-for instance with respect to the pre-CS baseline window duration, the start, and end of the peak detection window. We systematically scrutinize the robustness of results when applying different BLC approach specifications to one representative pre-existing data set (N = 118) in a (partly) pre-registered study. We report high agreement between different BLC approaches for US and CS+ trials, but moderate to poor agreement for CS- trials. Furthermore, a specification curve of the main effect of CS discrimination during fear acquisition training from all potential and reasonable combinations of specifications (N = 150) and a prototypical trough-to-peak (TTP) approach indicates that resulting effect sizes are largely comparable. A second specification curve (N = 605 specific combinations) highlights a strong impact of different transformation types. Crucially, however, we show that BLC approaches often misclassify the peak value-particularly for CS- trials, leading to stimulus-specific biases and challenges for post-processing and replicability of CS discrimination across studies applying different approaches. Lastly, we investigate how negative skin conductance values in BLC, appearing most frequently for CS- (CS- &gt; CS+ &gt; US), correspond to values in TTP quantification. We discuss the results considering prospects and challenges of the multiverse approach and future directions.

Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review

This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six scientific databases, fifty-nine were finally selected according to various criteria established. The systematic review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, pre-processing, processing and feature extraction. Finally, all ML techniques applied to the features of these signals for arousal classification have been studied. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high-performance values. In contrast, it has been shown that unsupervised learning is not present in the detection of arousal through EDA. This systematic review concludes that the use of EDA for the detection of arousal is widely spread, with particularly good results in classification with the ML methods found.

Electrodermal Activity of Preschool-Age Children Who Stutter During a Child-Friendly Stroop Paradigm

PURPOSE

The aim of the study was to assess whether emotional reactivity, indexed by a distinct physiological measure of sympathetic activation, differs between preschool-age children who stutter (CWS) and preschool-age children who do not stutter (CWNS) during a child-friendly Stroop task (i.e., day-night task). Additionally, researchers aimed to assess whether the Stroop task, compared to a control task, was a significant physiological stressor.

METHOD

Fifteen preschool-age CWS and 22 preschool-age CWNS were asked to perform a day-night Stroop task in order to elicit psychophysiological reactivity, indexed by electrodermal response (EDR) occurrence frequency and EDR amplitude. Physiological measurements were recorded during pretask baselines, performance of the day-night Stroop task, and performance of a speech-language control task.

RESULTS

Findings based on EDR measures did not support the hypothesis that the child-friendly day-night Stroop task is an effective stressor as compared to a control task based on measures of physiological arousal in preschool-age children. The CWS and CWNS did not significantly differ in their EDR measures relative to the control task or Stroop task (p > .05). However, CWS, compared to CWNS, exhibited significantly greater EDR amplitudes during the control task baseline (p < .05) and the Stroop task baseline (p < .05).

CONCLUSION

Overall, these findings may suggest that a predisposition to heightened levels of sympathetic activity prior to tasks in preschool-age CWS is important to consider with regard to the nature of developmental stuttering.

Design and Validation of a Multimodal Wearable Device for Simultaneous Collection of Electrocardiogram, Electromyogram, and Electrodermal Activity

Bio-signals are being increasingly used for the assessment of pathophysiological conditions including pain, stress, fatigue, and anxiety. For some approaches, a single signal is not sufficient to provide a comprehensive diagnosis; however, there is a growing consensus that multimodal approaches allow higher sensitivity and specificity. For instance, in visceral pain subjects, the autonomic activation can be inferred using electrodermal activity (EDA) and heart rate variability derived from the electrocardiogram (ECG), but including the muscle activation detected from the surface electromyogram (sEMG) can better differentiate the disease that causes the pain. There is no wearable device commercially capable of collecting these three signals simultaneously. This paper presents the validation of a novel multimodal low profile wearable data acquisition device for the simultaneous collection of EDA, ECG, and sEMG signals. The device was validated by comparing its performance to laboratory-scale reference devices. N = 20 healthy subjects were recruited to participate in a four-stage study that exposed them to an array of cognitive, orthostatic, and muscular stimuli, ensuring the device is sensitive to a range of stressors. Time and frequency domain analyses for all three signals showed significant similarities between our device and the reference devices. Correlation of sEMG metrics ranged from 0.81 to 0.95 and EDA/ECG metrics showed few instances of significant difference in trends between our device and the references. With only minor observed differences, we demonstrated the ability of our device to collect EDA, sEMG, and ECG signals. This device will enable future practical and impactful advances in the field of chronic pain and stress measurement and can confidently be implemented in related studies.

New insights on the correspondence between subjective affective experience and physiological responses from representational similarity analysis

Classical views suggest that experienced affect is related to a specific bodily response, whereas recent perspectives challenge this view postulating that similar affective experiences rather evoke different physiological responses. To further advance this debate in the field, we used representational similarity analysis to investigate the correspondence between subjective affect (arousal and valence ratings) and physiological reactions (skin conductance response [SCR], startle blink response, heart rate, and corrugator activity) across various emotion induction contexts (picture viewing task, sound listening task, and imagery task). Significant similarities were exclusively observed between SCR and arousal in the picture viewing task. However, none of the other physiological measures showed a significant relation with valence and arousal ratings in any of the tasks. These findings are discussed within the framework of the Populations hypothesis, suggesting that physiological responses do not depend on the experienced affect but are directly associated with the context in which they are evoked.

ComEDA: A new tool for stress assessment based on electrodermal activity

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.

The impact of black carbon (BC) on mode-specific galvanic skin response (GSR) as a measure of stress in urban environments

Previous research has shown that walking and cycling could help alleviate stress in cities, however there is poor knowledge on how specific microenvironmental conditions encountered during daily journeys may lead to varying degrees of stress experienced at that moment. We use objectively measured data and a robust causal inference framework to address this gap. Using a Bayesian Doubly Robust (BDR) approach, we find that black carbon exposure statistically significantly increases stress, as measured by Galvanic Skin Response (GSR), while cycling and while walking. Augmented Outcome Regression (AOR) models indicate that greenspace exposure and the presence of walking or cycling infrastructure could reduce stress. None of these effects are statistically significant for people in motorized transport. These findings add to a growing evidence-base on health benefits of policies aimed at decreasing air pollution, improving active travel infrastructure and increasing greenspace in cities.

Galvanic Skin Response Features in Psychiatry and Mental Disorders: A Narrative Review

This narrative review is aimed at presenting the galvanic skin response (GSR) Biofeedback method and possibilities for its application in persons with mental disorders as a modern form of neurorehabilitation. In the treatment of mental disorders of various backgrounds and courses, attention is focused on methods that would combine pharmacological treatment with therapies improving functioning. Currently, the focus is on neuronal mechanisms which, being physiological markers, offer opportunities for correction of existing deficits. One such indicator is electrodermal activity (EDA), providing information about emotions, cognitive processes, and behavior, and thus, about the function of various brain regions. Measurement of the galvanic skin response (GSR), both skin conductance level (SCL) and skin conductance responses (SCR), is used in diagnostics and treatment of mental disorders, and the training method itself, based on GSR Biofeedback, allows for modulation of the emotional state depending on needs occurring. Summary: It is relatively probable that neurorehabilitation based on GSR-BF is a method worth noticing, which-in the future-can represent an interesting area of rehabilitation supplementing a comprehensive treatment for people with mental disorders.

SUDOSCAN, an Innovative, Simple and Non-Invasive Medical Device for Assessing Sudomotor Function

Diabetic autonomic neuropathy is probably the most undiagnosed but serious complication of diabetes. The main objectives were to assess the prevalence of peripheral and autonomic neuropathy in a population of diabetic patients, analyze it in a real-life outpatient unit scenario and determine the feasibility of performing SUDOSCAN tests together with widely used tests for neuropathy. A total of 33 patients were included in the study. Different scoring systems (the Toronto Clinical Neuropathy Score-TCNS; the Neuropathy Disability Score-NDS; and the Neuropathy Symptom Score-NSS) were applied to record diabetic neuropathy (DN), while the SUDOSCAN medical device was used to assess sudomotor function, detect diabetic autonomic neuropathy and screen for cardiac autonomic neuropathy (CAN). Fifteen (45.5%) patients had sudomotor dysfunction. The SUDOSCAN CAN risk score was positively correlated with the hands' electrochemical sweat conductance (ESC), diastolic blood pressure (DBP), the level of the glycated hemoglobin, as well as with the TCNS, NDS and NSS. Performing SUDOSCAN tests together with other tests for DN proved to be a feasible approach that could be used in daily clinical practice in order to screen for DN, as well as for the early screening of CAN, before more complex and time-consuming tests.

Toward Mental Effort Measurement Using Electrodermal Activity Features

The ability to monitor mental effort during a task using a wearable sensor may improve productivity for both work and study. The use of the electrodermal activity (EDA) signal for tracking mental effort is an emerging area of research. Through analysis of over 92 h of data collected with the Empatica E4 on a single participant across 91 different activities, we report on the efficacy of using EDA features getting at signal intensity, signal dispersion, and peak intensity for prediction of the participant's self-reported mental effort. We implemented the logistic regression algorithm as an interpretable machine learning approach and found that features related to signal intensity and peak intensity were most useful for the prediction of whether the participant was in a self-reported high mental effort state; increased signal and peak intensity were indicative of high mental effort. When cross-validated by activity moderate predictive efficacy was achieved (AUC = 0.63, F1 = 0.63, precision = 0.64, recall = 0.63) which was significantly stronger than using the model bias alone. Predicting mental effort using physiological data is a complex problem, and our findings add to research from other contexts showing that EDA may be a promising physiological indicator to use for sensor-based self-monitoring of mental effort throughout the day. Integration of other physiological features related to heart rate, respiration, and circulation may be necessary to obtain more accurate predictions.

Revisiting sex differences in the acquisition and extinction of threat conditioning in humans

Findings pertaining to sex differences in the acquisition and extinction of threat conditioning, a paradigm widely used to study emotional homeostasis, remain inconsistent, particularly in humans. This inconsistency is likely due to multiple factors, one of which is sample size. Here, we pooled functional magnetic resonance imaging (fMRI) and skin conductance response (SCR) data from multiple studies in healthy humans to examine sex differences during threat conditioning, extinction learning, and extinction memory recall. We observed increased functional activation in males, relative to females, in multiple parietal and frontal (medial and lateral) cortical regions during acquisition of threat conditioning and extinction learning. Females mainly exhibited higher amygdala activation during extinction memory recall to the extinguished conditioned stimulus and also while responding to the unconditioned stimulus (presentation of the shock) during threat conditioning. Whole-brain functional connectivity analyses revealed that females showed increased connectivity across multiple networks including visual, ventral attention, and somatomotor networks during late extinction learning. At the psychophysiological level, a sex difference was only observed during shock delivery, with males exhibiting higher unconditioned responses relative to females. Our findings point to minimal to no sex differences in the expression of conditioned responses during acquisition and extinction of such responses. Functional MRI findings, however, show some distinct functional activations and connectivities between the sexes. These data suggest that males and females might use different neural mechanisms, mainly related to cognitive processing, to achieve comparable levels of acquired conditioned responses to threating cues.

Event-related driver stress detection with smartphones among young novice drivers

Complex and diverse driving situations can pose short-term stressors to novice drivers. Continuously detecting stress is essential for driver training, stress intervention, and the design of in-vehicle information systems. This study designed and validated a driver stress detection method at the event level based on machine learning algorithms and facial features captured with smartphones. Thirty young novice drivers completed two driving tasks containing eight events of two versions (neutral and stressful), with psychological, physiological, and facial data collected. Four combinations of input data types and six machine learning algorithms were used to detect stressful events. The KNN algorithm with facial plus individual profile features yielded the highest accuracy of 89.2%. Adding individual profile features can improve classification performance. Facial areas such as brow, eye, jaw, nose, and mouth were most sensitive to stress. This approach could provide more temporal-spatial information about the driver's stress levels during the whole driving process. Practitioner Summary: This paper proposed a method to detect driver stress at the event level with smartphones. Models with facial plus individual profile features and the KNN algorithm had the most outstanding classification performance. The presented approach can serve as a tool for improving in-vehicle interaction system design when considering driver stress. Abbreviations: GSR: galvanic skin response; ECG: electrocardiography; HR: heart rate; HRV: heart rate variability; RGB: red green blue; NIR: near-infrared; IP: individual profile; DSI: driver stress inventory; APS: arousal predisposition scale; API: application programming interface; PPG: photoplethysmography; EDR: electrodermal response; PD: pupil diameter; SCL: skin conductance level; RF: random forest; KNN: k-nearest neighbour; LDA: linear discriminant analysis; QDA: quadratic discriminant analysis; SVML: support vector machines with the linear kernel; SVMP: support vector machines with the polynomial kernel; TP: true positive; TN: true negative; FP: false positive; FN: false negative; t-SNE: t-distributed stochastic neighbour embedding.

Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference

Electrodermal activities (EDA) are any electrical phxenomena observed on the skin. Skin conductance (SC), a measure of EDA, shows fluctuations due to autonomic nervous system (ANS) activation induced sweat secretion. Since it can capture psychophysiological information, there is a significant rise in the research work for tracking mental and physiological health with EDA. However, the current state-of-the-art lacks a physiologically motivated approach for real-time inference of ANS activation from EDA. Therefore, firstly, we propose a comprehensive model for the SC dynamics. The proposed model is a 3D state-space representation of the direct secretion of sweat via pore opening and diffusion followed by corresponding evaporation and reabsorption. As the input to the model, we consider a sparse signal representing the ANS activation that causes the sweat glands to produce sweat. Secondly, we derive a scalable fixed-interval smoother-based sparse recovery approach utilizing the proposed comprehensive model to infer the ANS activation enabling edge computation. We incorporate a generalized-cross-validation to tune the sparsity level. Finally, we propose an Expectation-Maximization based deconvolution approach for learning the model parameters during the ANS activation inference. For evaluation, we utilize a dataset with 26 participants, and the results show that our comprehensive state-space model can successfully describe the SC variations with high scalability, showing the feasibility of real-time applications. Results validate that our physiology-motivated state-space model can comprehensively explain the EDA and outperforms all previous approaches. Our findings introduce a whole new perspective and have a broader impact on the standard practices of EDA analysis.

The current state-of-the-art lacks physiology-motivated models for electrodermal activities (EDA) that have the power to comprehensively describe the variations in skin conductance (SC)–a measure of EDA. In this study, we propose a physiology-motivated state-space model to address previous challenges. On the other hand, there is also an absence of a scalable autonomic nervous system (ANS) activation inference method that simultaneously solve for the physiological system parameters. Furthermore, we develop a scalable ANS activation inference approach based on the proposed model with a goal for real-time edge computation. We utilize a dataset with 26 participants to validate the new model and the scalable method. Results demonstrate that our physiology-motivated state-space model can comprehensively explain the EDA. Our findings introduce a whole new perspective and have a broader impact on standard practices of EDA analysis.

A Deep Convolutional Autoencoder for Automatic Motion Artifact Removal in Electrodermal Activity Signals: A Preliminary Study

Automatic motion artifact (MA) removal in electrodermal activity (EDA) signals is a major challenge because of the aperiodic and irregular characteristics of EDA. Given the lack of a suitable MA removal algorithm, a substantial amount of EDA data is typically discarded, especially during ambulatory monitoring. Current methods for MA removal in EDA are feasible when data are corrupted with low magnitude artifacts. In this study, we propose a more data-driven deep convolutional autoencoder (DCAE) for automated motion artifact removal in EDA signals. The DCAE was trained using several publicly available datasets. We used both Gaussian white noise (GWN) and real-life induced MA data records collected in a laboratory setting to corrupt the clean EDA signals. We compared the performance of our DCAE network with three state-of-the-art methods using the performance metrics the signal-to-noise ratio (SNR) improvement (SNR_imp), and the mean squared error (MSE). The proposed DCAE provided significantly higher SNR_impand lower MSE compared to three other methods for both synthetically and real-life induced MA. While the work is preliminary, this work illustrates a promising approach which can potentially be used to remove many different types of MA.

Modeling subjective fear using skin conductance: a preliminary study in virtual reality

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.

Tonic Electrodermal Activity is a Robust Marker of Psychological and Physiological Changes during Induction of Anesthesia

Electrodermal activity (EDA), which tracks sweat gland activity as a proxy for sympathetic activation, has the potential to be a biomarker of physiological and psychological changes in the clinic. To show this, in this study, we demonstrate that the tonic component of EDA responds consistently and robustly during induction of anesthesia in the operating room in 8 subjects during surgery. This response is seen bilaterally. The response shows a significant increase in EDA in anticipation of induction and then a gradual decrease in response to the administration of medication, which agrees with both the expected psychological effects of stress and anxiety and the physiological effects of anesthetic medication on sweat glands. The results also show a slightly faster response to drug in the arm directly receiving the medication intravenously compared to the opposite, though the magnitude of the effect evens out over time. Clinical Relevance- EDA can serve as a robust non-invasive biomarker in the clinic to track both psychologically and physiologically induced autonomic changes.

Multi-level Pain Quantification using a Smartphone and Electrodermal Activity

Appropriate prescription of pain medication is challenging because pain is difficult to quantify due to the subjectiveness of pain assessment. Currently, clinicians must entirely rely on pain scales based on patients' assessments. This has been alleged to be one of the causes of drug overdose and addiction, and a contributor to the opioid crisis. Therefore, there is an urgent unmet need for objective pain assessment. Furthermore, as pain can occur anytime and anywhere, ambulatory pain monitoring would be welcomed in practice. In our previous study, we developed electrodermal activity (EDA)-derived indices and implemented them in a smartphone application that can communicate via Bluetooth to an EDA wearable device. While we previously showed high accuracy for high-level pain detection, multi-level pain detection has not been demonstrated. In this paper, we tested our smartphone application with a multi-level pain-induced dataset. The dataset was collected from fifteen subjects who underwent four levels of pain-inducing electrical pulse (EP) stimuli. We then performed statistical analyses and machine-learning techniques to classify multiple pain levels. Significant differences were observed in our EDA-derived indices among no-pain, low-pain, and high-pain segments. A random forest classifier showed 62.6% for the balanced accuracy, and a random forest regressor exhibited 0.441 for the coefficient of determination. Clinical Relevance - This is one of the first studies to present a smartphone application for detecting multiple levels of pain in real time using an EDA wearable device. This work shows the feasibility of ambulatory pain monitoring which can potentially be useful for chronic pain management.

Emotion Recognition Based on Energy-related Features of Peripheral Physiological Signals

The interest in development of methods and tools for recognizing human emotions has increased continuously. Using physiological information, especially the peripheral physiological signals, to identify emotions is an important direction for this area. This paper proposes an approach for emotion recognition based on energy-related features extracted from peripheral physiological signals. Three emotions: calm, happiness and fear, were elicited in 54 volunteers using video clips while three peripheral physiological signals were recorded: Electrocardiography (ECG), Photoplethysmography (PPG) and Respiration. Given that energy-related features of physiological signals are closely related to autonomic nervous systems activities, nine energy-related features were extracted from the recorded physiological signals. To find the optimal feature subset to represent the target emotions, the correlation between features and emotion state, as well as the discrimination ability of feature for emotion recognition were both analyzed. Four optimal features were then selected for further classification. Moreover, models based on Decision Tree (DT) were built to evaluate the performance of these features for purpose of recognition of emotion states of calm, happiness, and fear. The results show that the DT models based on these four optimal features could distinguish fear from calm (AUC=0.879, Accuracy=87.8%), happiness from calm (AUC=0.915, Accuracy=91.8%), and fear from happiness (AUC=0.822, Accuracy=81.8%), with a global recognition accuracy of 70.8%. These results indicate that energy-related features of peripheral physiological signals can reliably identify emotions, especially intense emotions.

Analysis of the skin conductance and pupil signals for evaluation of emotional elicitation by images and sounds

Many studies in the literature attempt recognition of emotions through the use of videos or images, but very few have explored the role that sounds have in evoking emotions. In this study we have devised an experimental protocol for elicitation of emotions by using, separately and jointly, images and sounds from the widely used International Affective Pictures System and International Affective Digital Sounds databases. During the experiments we have recorded the skin conductance and pupillary signals and processed them with the goal of extracting indices linked to the autonomic nervous system, thus revealing specific patterns of behavior depending on the different stimulation modalities. Our results show that skin conductance helps discriminate emotions along the arousal dimension, whereas features derived from the pupillary signal are able to discriminate different states along both valence and arousal dimensions. In particular, the pupillary diameter was found to be significantly greater at increasing arousal and during elicitation of negative emotions in the phases of viewing images and images with sounds. In the sound-only phase, on the other hand, the power calculated in the high and very high frequency bands of the pupillary diameter were significantly greater at higher valence (valence ratings > 5). Clinical relevance- This study demonstrates the ability of physiological signals to assess specific emotional states by providing different activation patterns depending on the stimulation through images, sounds and images with sounds. The approach has high clinical relevance as it could be extended to evaluate mood disorders (e.g. depression, bipolar disorders, or just stress), or to use physiological patterns found for sounds in order to study whether hearing aids can lead to increased emotional perception.

An Effective Entropy-assisted Mind-wandering Detection System using EEG Signals of MM-SART Database

Mind-wandering (MW), which is usually defined as a lapse of attention has negative effects on our daily life. Therefore, detecting when MW occurs can prevent us from those negative outcomes resulting from MW. In this work, we first collected a multi-modal Sustained Attention to Response Task (MM-SART) database for MW detection. Eighty-two participants' data were collected in our dataset. For each participant, we collected measures of 32-channels electroencephalogram (EEG) signals, photoplethysmography (PPG) signals, galvanic skin response (GSR) signals, eye tracker signals, and several questionnaires for detailed analyses. Then, we propose an effective MW detection system based on the collected EEG signals. To explore the non-linear characteristics of the EEG signals, we utilize entropy-based features. The experimental results show that we can reach 0.712 AUC score by using the random forest (RF) classifier with the leave-one-subject-out cross-validation. Moreover, to lower the overall computational complexity of the MW detection system, we propose correlation importance feature elimination (CIFE) along with AUC-based channel selection. By using two most significant EEG channels, we can reduce the training time of the classifier by 44.16%. By applying CIFE on the feature set, we can further improve the AUC score to 0.725 but with only 14.6% of the selection time compared with the recursive feature elimination (RFE). Finally, we can apply the current work to educational scenarios nowadays, especially in remote learning systems.

Electrodermal Activity Moderates Sleep-Behavior Associations in Children with Autism Spectrum Disorder

Relative to children without autism spectrum disorder (ASD), children with ASD experience elevated sleep problems that can contribute to behavioral comorbidities. This study explored the interaction between psychophysiology and sleep to determine which children with ASD may be at risk for, or resilient to, effects of poor sleep on daytime behavior. Participants included 48 children (aged 6-10 years) with ASD. Measures of sympathetic nervous system activity (electrodermal activity; EDA) were collected during a baseline and in response to a laboratory challenge task. Parents reported on their children's sleep problems and behavioral functioning, including broad externalizing symptoms and situational noncompliance, using standardized questionnaires and a clinical interview. EDA moderated the significant positive associations between sleep problems and both behavioral outcomes. The link between sleep problems and broad externalizing symptoms and situational noncompliance was positive and significant in the context of lower baseline EDA and nonsignificant in the context of higher baseline EDA. Sleep problems also interacted with EDA reactivity in predicting situational noncompliance, but not broad externalizing symptoms. Findings highlight the complex interplay among sleep, daytime behavior, and psychophysiology in children with ASD. Results are interpreted in the context of differential susceptibility and dual-risk frameworks. This study underscores the importance of high-quality sleep for children with ASD, especially those with the biological sensitivity or vulnerability factors (i.e., EDA) identified in this study. Clinical implications are discussed, and directions for future research are provided.

Classification of Dichotomous Emotional States Using Electrodermal Activity Signals and Multispectral Analysis

In this work, an analysis based on complex demodulation is proposed to classify dichotomous emotional states using Electrodermal activity (EDA) signals. For this, annotated happy and sad EDA is obtained from an online public database. The sympathetic activity indices, namely Time-varying (TVSymp) and Modified TVSymp, are computed from the reconstructed EDA signal. Further, the derivative of phasic EDA is calculated from the phasic component obtained using the convex optimization (cvxEDA) based EDA decomposition method. Five statistical features are computed from each index and used for the classification. The results of the classification indicate that these features are capable of differentiating happy and sad emotional states with 75% accuracy. This technique could be effective in the identification of clinical disorders associated with happy and sad emotional states.

Methodological implications of sample size and extinction gradient on the robustness of fear conditioning across different analytic strategies

Fear conditioning paradigms are critical to understanding anxiety-related disorders, but studies use an inconsistent array of methods to quantify the same underlying learning process. We previously demonstrated that selection of trials from different stages of experimental phases and inconsistent use of average compared to trial-by-trial analysis can deliver significantly divergent outcomes, regardless of whether the data is analysed with extinction as a single effect, as a learning process over the course of the experiment, or in relation to acquisition learning. Since small sample sizes are attributed as sources of poor replicability in psychological science, in this study we aimed to investigate if changes in sample size influences the divergences that occur when different kinds of fear conditioning analyses are used. We analysed a large data set of fear acquisition and extinction learning (N = 379), measured via skin conductance responses (SCRs), which was resampled with replacement to create a wide range of bootstrapped databases (N = 30, N = 60, N = 120, N = 180, N = 240, N = 360, N = 480, N = 600, N = 720, N = 840, N = 960, N = 1080, N = 1200, N = 1500, N = 1750, N = 2000) and tested whether use of different analyses continued to produce deviating outcomes. We found that sample size did not significantly influence the effects of inconsistent analytic strategy when no group-level effect was included but found strategy-dependent effects when group-level effects were simulated. These findings suggest that confounds incurred by inconsistent analyses remain stable in the face of sample size variation, but only under specific circumstances with overall robustness strongly hinging on the relationship between experimental design and choice of analyses. This supports the view that such variations reflect a more fundamental confound in psychological science—the measurement of a single process by multiple methods.

A Deep Convolutional Autoencoder for Automatic Motion Artifact Removal in Electrodermal Activity

OBJECTIVE

This study aimed to develop a robust and data driven automatic motion artifacts (MA) removal technique from electrodermal activity (EDA) signal.

METHODS

we proposed a deep convolutional autoencoder (DCAE) approach for automatic MA removal in EDA signals. Our model was trained using several publicly available datasets that were collected using a wide variety of stimuli to cause EDA reactions; the sample size was large (N=385 subjects). We trained and validated our DCAE network using both Gaussian white noise (GWN) and realistic MA data records collected using a novel circuitry in our lab. We further evaluated and compared the performance of our DCAE model with the existing methods on two independent and unseen datasets called Chon lab motion artifact dataset II (CMAD II) and central nervous system oxygen toxicity dataset (CNS-OT).

RESULTS

Our DCAE model showed significantly higher signal-to-noise-power-ratio improvement (SNR_imp) and lower mean squared error (MSE) when compared with that of the three previous methods (averaged SNR_imp=35.25 dB, and MSE=0.028 on the MA-corrupted data). Moreover, the reconstructed EDAs from the CMAD II dataset had a mean correlation value of 0.78 (statistically significantly higher when compared with other methods) with the reference clean data from the motionless hand, whereas the raw MA-corrupted data had a correlation value of only 0.68.

CONCLUSION

The results presented in the paper indicates that our DCAE can remove MAs with higher intensity where the existing methods fails.

SIGNIFICANCE

Proposed DCAE model can be used to recover a significant amount of otherwise discarded EDA data.

Comparison of Electrodermal Activity from Multiple Body Locations Based on Standard EDA Indices' Quality and Robustness against Motion Artifact

The most traditional sites for electrodermal activity (EDA) data collection, palmar locations such as fingers or palms, are not usually recommended for ambulatory monitoring given that subjects have to use their hands regularly during their daily activities, and therefore, alternative sites are often sought for EDA data collection. In this study, we collected EDA signals (n = 23 subjects, 19 male) from four measurement sites (forehead, back of neck, finger, and inner edge of foot) during cognitive stress and induction of mild motion artifacts by walking and one-handed weightlifting. Furthermore, we computed several EDA indices from the EDA signals obtained from different sites and evaluated their efficiency to classify cognitive stress from the baseline state. We found a high within-subject correlation between the EDA signals obtained from the finger and the feet. Consistently high correlation was also found between the finger and the foot EDA in both the phasic and tonic components. Statistically significant differences were obtained between the baseline and cognitive stress stage only for the EDA indices computed from the finger and the foot EDA. Moreover, the receiver operating characteristic curve for cognitive stress detection showed a higher area-under-the-curve for the EDA indices computed from the finger and foot EDA. We also evaluated the robustness of the different body sites against motion artifacts and found that the foot EDA location was the best alternative to other sites.

The cerebellum contributes to context-effects during fear extinction learning: a 7T fMRI study

The cerebellum is involved in the acquisition and consolidation of learned fear responses. Knowledge about its contribution to extinction learning, however, is sparse. Extinction processes likely involve erasure of memories, but there is ample evidence that at least part of the original memory remains. We asked the question whether memory persists within the cerebellum following extinction training. The renewal effect, that is the reoccurrence of the extinguished fear memory during recall in a context different from the extinction context, constitutes one of the phenomena indicating that memory of extinguished learned fear responses is not fully erased during extinction training. We performed a differential AB-A/B fear conditioning paradigm in a 7-Tesla (7T) MRI system in 31 young and healthy men. On day 1, fear acquisition training was performed in context A and extinction training in context B. On day 2, recall was tested in contexts A and B. As expected, participants learned to predict that the CS+ was followed by an aversive electric shock during fear acquisition training. Skin conductance responses (SCRs) were significantly higher to the CS+ compared to the CS- at the end of acquisition. Differences in SCRs vanished in extinction and reoccurred in the acquisition context during recall indicating renewal. Fitting SCR data, a deep neural network model was trained to predict the correct shock value for a given stimulus and context. Event-related fMRI analysis with model-derived prediction values as parametric modulations showed significant effects on activation of the posterolateral cerebellum (lobules VI and Crus I) during recall. Since the prediction values differ based on stimulus (CS+ and CS-) and context during recall, data provide support that the cerebellum is involved in context-related recall of learned fear associations. Likewise, mean β values were highest in lobules VI and Crus I bilaterally related to the CS+ in the acquisition context during early recall. A similar pattern was seen in the vermis, but only on a trend level. Thus, part of the original memory likely remains within the cerebellum following extinction training. We found cerebellar activations related to the CS+ and CS- during fear acquisition training which likely reflect associative and non-associative aspects of the task. Cerebellar activations, however, were not significantly different for CS+ and CS-. Since the CS- was never followed by an electric shock, the cerebellum may contribute to associative learning related to the CS, for example as a safety cue.

Electrodermal activity spectral and nonlinear analysis - potential biomarkers for sympathetic dysregulation in autism

Autism spectrum disorder (ASD) is a neurodevelopmental disease characterized by emotional and social deficits, which can be associated with sympathetic dysregulation. Thus, we aimed to analyze the electrodermal activity (EDA) using time, and novel spectral and nonlinear indices in ASD. The cohort consisted of 45 ASD boys and 45 age-matched controls. EDA was continuously recorded at rest. The EDA indices were evaluated by time-, spectral-, and nonlinear-domain analysis. Our results revealed increased non-specific skin conductance responses, spectral parameters in high and very-high frequency bands, approximate and symbolic information entropy indicating sympathetic overactivity in ASD vs. controls (p < 0.05, for all). Surprisingly, the nonlinear index from detrended fluctuation analysis α1 was lower in ASD vs. controls (p = 0.024) providing thus distinct information about qualitative features of complex sympathetic regulation. Concluding, the complex time, spectral, and nonlinear EDA indices revealed discrete abnormalities in sympathetic cholinergic regulation as one of the potential pathomechanisms contributing to cardiovascular complications in ASD.

Assessment of diabetic small-fiber neuropathy by using short-wave infrared hyperspectral imaging

Among patients with type 2 diabetes mellitus (T2DM), the association between hyperspectral imaging (HSI) examination and diabetic neuropathy (DN) is ascertained using HSI of the feet using four types of spectral difference measurements. DN was evaluated by traditional Michigan Neuropathy Screening Instrument (MNSI), evaluation of painful neuropathy (ID-Pain, DN4) and sudomotor function by measuring electrochemical skin conductance (ESC). Of the 120 T2DM patients and 20 healthy adults enrolled, T2DM patients are categorized into normal sudomotor (ESC >60 μS) and sudomotor dysfunction (ESC ≤ 60 μS) groups. Spectral difference analyses reveal significant intergroup differences, whereas traditional examinations cannot distinguish between the two groups. HSI waveform reflectance gradually increases with disease severity, at 1400 to 1600 nm. The area under the curve (AUC) of receiver operating characteristic (ROC) analysis for abnormal ESC is ≥0.8 for all four HSI methods. HSI could be an objective, sensitive, rapid, noninvasive and remote approach to identify early small-fiber DN.

Quantifying Occupational Stress in Intensive Care Unit Nurses: An Applied Naturalistic Study of Correlations Among Stress, Heart Rate, Electrodermal Activity, and Skin Temperature

OBJECTIVE

To identify physiological correlates to stress in intensive care unit nurses.

BACKGROUND

Most research on stress correlates are done in laboratory environments; naturalistic investigation of stress remains a general gap.

METHOD

Electrodermal activity, heart rate, and skin temperatures were recorded continuously for 12-hr nursing shifts (23 participants) using a wrist-worn wearable technology (Empatica E4).

RESULTS

Positive correlations included stress and heart rate (ρ = .35, p < .001), stress and skin temperature (ρ = .49, p < .05), and heart rate and skin temperatures (ρ = .54, p = .0008).

DISCUSSION

The presence and direction of some correlations found in this study differ from those anticipated from prior literature, illustrating the importance of complementing laboratory research with naturalistic studies. Further work is warranted to recognize nursing activities associated with a high level of stress and the underlying reasons associated with changes in physiological responses.

APPLICATION

Heart rate and skin temperature may be used for real-time detection of stress, but more work is needed to validate such surrogate measures.

Lighting for work: a study on the effect of underground low-light environment on miners' physiology

Low-light environment affects human physiology, psychology, and behavior. It causes errors in work and increases accidents. In this study, we built a coal mine lighting simulation experiment system. The system not only includes an experimental environment simulation system and a physiological indicator test system, but also adds a miners' working simulation system. We aim to study the effect of different illumination levels (0lx, 10lx, 50lx, 100lx, and 200lx) on three indicators: heart rate, electrodermal activity, and respiration. The results show illuminance has a significant negative correlation with all the above three indicators. Heart rate seems to be most significantly affected by illuminance, and it changes significantly from the normal level (200lx) at 50lx. By contrast, the respiratory rate and electrodermal activity change significantly at 10lx. When the illuminance is 50~100lx, all the three indicators return to the normal level. The results suggest that coal mine illumination should be around 50~100lx. When the minimum illumination is less than 10lx, accidents tend to increase.

Mental imagery can generate and regulate acquired differential fear conditioned reactivity

Mental imagery is an important tool in the cognitive control of emotion. The present study tests the prediction that visual imagery can generate and regulate differential fear conditioning via the activation and prioritization of stimulus representations in early visual cortices. We combined differential fear conditioning with manipulations of viewing and imagining basic visual stimuli in humans. We discovered that mental imagery of a fear-conditioned stimulus compared to imagery of a safe conditioned stimulus generated a significantly greater conditioned response as measured by self-reported fear, the skin conductance response, and right anterior insula activity (experiment 1). Moreover, mental imagery effectively down- and up-regulated the fear conditioned responses (experiment 2). Multivariate classification using the functional magnetic resonance imaging data from retinotopically defined early visual regions revealed significant decoding of the imagined stimuli in V2 and V3 (experiment 1) but significantly reduced decoding in these regions during imagery-based regulation (experiment 2). Together, the present findings indicate that mental imagery can generate and regulate a differential fear conditioned response via mechanisms of the depictive theory of imagery and the biased-competition theory of attention. These findings also highlight the potential importance of mental imagery in the manifestation and treatment of psychological illnesses.

Inter-Brain Synchrony Levels According to Task Execution Modes and Difficulty Levels: an fNIRS/GSR Study

Hyperscanning is a brain imaging technique that measures brain synchrony caused by social interactions. Recent research on hyperscanning has revealed substantial inter-brain synchrony (IBS), but little is known about the link between IBS and mental workload. To study this link, we conducted an experiment consisting of button-pressing tasks of three different difficulty levels for the cooperation and competition modes with 56 participants aged 23.7±3.8 years (mean±standard deviation). We attempted to observe IBS using functional near-infrared spectroscopy (fNIRS) and galvanic skin response (GSR) to assess the activities of the human autonomic nervous system. We found that the IBS levels increased in a frequency band of 0.075-0.15 Hz, which was unrelated to the task repetition frequency in the cooperation mode according to the task difficulty level. Significant relative inter-brain synchrony (RIBS) increases were observed in three and 10 channels out of 15 for the hard tasks compared to the normal and easy tasks, respectively. We observed that the average GSR values increased with increasing task difficulty levels for the competition mode only. Thus, our results suggest that the IBS revealed by fNIRS and GSR is not related to the hemodynamic changes induced by mental workload, simple behavioral synchrony such as button-pressing timing, or autonomic nervous system activity. IBS is thus explicitly caused by social interactions such as cooperation.