Measurements of Electrodermal Activity, Tissue Oxygen Saturation, and Visual Analog Scale for Different Cuff Pressures

The quantification of comfort in binding parts, essential human-machine interfaces (HMI) for the functioning of rehabilitation robots, is necessary to reduce physical strain on the user despite great achievements in their structure and control. This study aims to investigate the physiological impacts of binding parts by measuring electrodermal activity (EDA) and tissue oxygen saturation (StO2). In Experiment 1, EDA was measured from 13 healthy subjects under three different pressure conditions (10, 20, and 30 kPa) for 1 min using a pneumatic cuff on the right thigh. In Experiment 2, EDA and StO2 were measured from 10 healthy subjects for 5 min. To analyze the correlation between EDA parameters and the decrease in StO2, a survey using the visual analog scale (VAS) was conducted to assess the level of discomfort at each pressure. The EDA signal was decomposed into phasic and tonic components, and the EDA parameters were extracted from these two components. RM ANOVA and a post hoc paired t-test were used to determine significant differences in parameters as the pressure increased. The results showed that EDA parameters and the decrease in StO2 significantly increased with the pressure increase. Among the extracted parameters, the decrease in StO2 and the mean SCL proved to be effective indicators. Such analysis outcomes would be highly beneficial for studies focusing on the comfort assessment of the binding parts of rehabilitation robots.

Harnessing Wearable Devices for Emotional Intelligence: Therapeutic Applications in Digital Health

Emotional intelligence strives to bridge the gap between human and machine interactions. The application of such systems varies and is becoming more prominent as healthcare services seek to provide more efficient care by utilizing smart digital health apps. One application in digital health is the incorporation of emotion recognition systems as a tool for therapeutic interventions. To this end, a system is designed to collect and analyze physiological signal data, such as electrodermal activity (EDA) and electrocardiogram (ECG), from smart wearable devices. The data are collected from different subjects of varying ages taking part in a study on emotion induction methods. The obtained signals are processed to identify stimulus trigger instances and classify the different reaction stages, as well as arousal strength, using signal processing and machine learning techniques. The reaction stages are identified using a support vector machine algorithm, while the arousal strength is classified using the ResNet50 network architecture. The findings indicate that the EDA signal effectively identifies the emotional trigger, registering a root mean squared error (RMSE) of 0.9871. The features collected from the ECG signal show efficient emotion detection with 94.19% accuracy. However, arousal strength classification is only able to reach 60.37% accuracy on the given dataset. The proposed system effectively detects emotional reactions and can categorize their arousal strength in response to specific stimuli. Such a system could be integrated into therapeutic settings to monitor patients' emotional responses during therapy sessions. This real-time feedback can guide therapists in adjusting their strategies or interventions.

Interactive slide selection algorithm and machine learning in psychophysiological memory testing

Objective. To present a new type of concealed information test (CIT) that implements the interactive slide selection (ISS) algorithm and compare its effectiveness with a standard CIT (sCIT).Approach. The ISS algorithm presents slides interactively, based on the analysis of electrodermal activity, while sCIT presents slides in a predefined, sequential order. The algorithm automatically selects irrelevant, relevant, and control slides and presents them at the moment which is physiologically most suitable for electrodermal response detection. To compare the ISS-based CIT (issCIT) and sCIT, two objects, a bag, and a wallet, were presented to 64 participants, 32 of whomwere analyzed with sCIT, and another 32 with issCIT.Main results. The results show that ISS had significantly better true/false predictions (Fisher's exact test,p< 0.01). Also, the number of false positives (FPs) was significantly lower in the issCIT group in comparison with sCIT (Fisher's exact test,p< 0.001). Machine learning (ML) classifiers improved precision from 49% to 79% in the sCIT group (McNemar's test,p< 0.05), and from 85% to 100% in the issCIT group (McNemar's test,p< 0.05). The testing time in the issCIT group ranged between 42 and 107 s, while the average was 53 s. In the sCIT group, the testing time was always 330 s.Significance. Under the presented experimental settings, the ISS algorithm obtained significantly better classification results compared to sCIT, while the application of the ML algorithms managed to improve the classification results in both groups reaching a precision of 100%. The ISS algorithm allowed for a much shorter testing time compared to sCIT.

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.

Associations Between Sympathetic Nervous System Synchrony, Movement Synchrony, and Speech in Couple Therapy

Background

Research on interpersonal synchrony has mostly focused on a single modality, and hence little is known about the connections between different types of social attunement. In this study, the relationship between sympathetic nervous system synchrony, movement synchrony, and the amount of speech were studied in couple therapy.

Methods

Data comprised 12 couple therapy cases (24 clients and 10 therapists working in pairs as co-therapists). Synchrony in electrodermal activity, head and body movement, and the amount of speech and simultaneous speech during the sessions were analyzed in 12 sessions at the start of couple therapy (all 72 dyads) and eight sessions at the end of therapy (48 dyads). Synchrony was calculated from cross-correlations using time lags and compared to segment-shuffled pseudo synchrony. The associations between the synchrony modalities and speech were analyzed using complex modeling (Mplus).

Findings

Couple therapy participants' synchrony mostly occurred in-phase (positive synchrony). Anti-phase (negative) synchrony was more common in movement than in sympathetic nervous system activity. Synchrony in sympathetic nervous system activity only correlated with movement synchrony between the client-therapist dyads (r = 0.66 body synchrony, r = 0.59 head synchrony). Movement synchrony and the amount of speech correlated negatively between spouses (r = -0.62 body synchrony, r = -0.47 head synchrony) and co-therapists (r = -0.39 body synchrony, r = -0.28 head synchrony), meaning that the more time the dyad members talked during the session, the less bodily synchrony they exhibited.

Conclusion

The different roles and relationships in couple therapy were associated with the extent to which synchrony modalities were linked with each other. In the relationship between clients and therapists, synchrony in arousal levels and movement "walked hand in hand", whereas in the other relationships (spouse or colleague) they were not linked. Generally, more talk time by the therapy participants was associated with anti-phase movement synchrony. If, as suggested, emotions prepare us for motor action, an important finding of this study is that sympathetic nervous system activity can also synchronize with that of others independently of motor action.

Interpersonal Physiological Synchrony for Detecting Moments of Connection in Persons With Dementia: A Pilot Study

Interpersonal physiological synchrony has been successfully used to characterize social interactions and social processes during a variety of interpersonal interactions. There are a handful of measures of interpersonal physiological synchrony, but those that exist have only been validated on able-bodied adults. Here, we present a novel information-theory based measure of interpersonal physiological synchrony-normalized Symbolic Transfer Entropy (NSTE)-and compare its performance with a popular physiological synchrony measure-physiological concordance and single session index (SSI). Using wearable sensors, we measured the electrodermal activity (EDA) of five individuals with dementia and six able-bodied individuals as they participated in a movement activity that aimed to foster connection in persons with dementia. We calculated time-resolved NSTE and SSI measures for case studies of three dyads and compared them against moments of observed interpersonal connection in video recordings of the activity. Our findings suggest that NSTE-based measures of interpersonal physiological synchrony may provide additional advantages over SSI, including resolving moments of ambiguous SSI and providing information about the direction of information flow between participants. This study also investigated the feasibility of using interpersonal synchrony to gain insight into moments of connection experienced by individuals with dementia and further encourages exploration of these measures in other populations with reduced communicative abilities.

Assessment of Resilience of the Hellenic Navy Seals by Electrodermal Activity during Cognitive Tasks

Stress resilience plays a key role in task performance during emergencies, especially in occupations like military special forces, with a routine consisting of unexpected events. Nevertheless, reliable and applicable measurements of resilience in predicting task performance in stressful conditions are still researched. This study aimed to explore the stress response in the Hellenic Navy SEALs (HN-SEALs), using a cognitive–physiological approach. Eighteen candidates under intense preparation for their enlistment in the HN-SEALs and 16 healthy controls (HCs) underwent Stroop tests, along with mental-state and personality examination. Simultaneously, electrodermal activity (EDA) was assessed during each one of cognitive testing procedures. Compared to healthy control values, multiple components of EDA values were found decreased (p < 0.05) in the HN-SEALs group. These results were associated with an increase in resilience level in the HN-SEALs group, since a restricted sympathetic reactivity according to the reduced EDA values was observed during the stressful cognitive testing. This is the first report providing physiological measurements of the sympathetic response of HN-SEALs to a stressful situation and suggests that EDA turns out to be a simple and objective tool of sympathetic activation and it may be used as a complementary index of resilience in HN-SEALs candidates.

A Naturalistic, European Multi-Center Clinical Study of Electrodermal Reactivity and Suicide Risk Among Patients With Depression

Background: Electrodermal hyporeactivity has been proposed as a marker of suicidal risk. The EUDOR-A study investigated the prevalence of electrodermal hyporeactivity among patients with depression and its association with attempted and completed suicide. Methods: Between August 2014 and March 2016, 1,573 in- and outpatients with a primary diagnosis of depression (active or remission phase) were recruited at 15 European psychiatric centers. Each patient was followed-up for 1 year. Electrodermal activity was assessed at baseline with the ElectroDermal Orienting Reactivity Test. Data on the sociodemographic characteristics, clinical diagnoses, and treatment of the subjects were also collected. The severity of the depressive symptoms was assessed through the Montgomery-Asberg Depression Rating Scale. Information regarding number, time, and method of suicide attempts was gathered at baseline and at the end of the 1-year follow-up. The same data were collected in case of completed suicide. Results: Hyporeactive patients were shown to be significantly more at risk of suicide attempt compared to reactive patients, both at baseline and follow-up. A sensitivity of 29.86% and a positive predictive value (PPV) of 46.77% were found for attempted suicide at baseline, while a sensitivity of 35.36% and a PPV of 8.92% were found for attempted suicide at follow-up. The sensitivity and PPV for completed suicide were 25.00 and 0.61%, respectively. However, when controlled for suicide attempt at baseline, the association between hyporeactivity and follow-up suicide attempt was no longer significant. The low number of completed suicides did not allow any analysis.

Neuroergonomic Assessment of Hot Beverage Preparation and Consumption: An EEG and EDA Study

Neuroergonomics is an emerging field that investigates the human brain about behavioral performance in natural environments and everyday settings. This study investigated the body and brain activity correlates of a typical daily activity, hot beverage preparation, and consumption in a realistic office environment where participants performed natural daily tasks. Using wearable, battery operated and wireless Electroencephalogram (EEG) and Electrodermal activity (EDA) sensors, neural and physiological responses were measured in untethered, freely moving participants who prepared hot beverages using two different machines (a market leader and follower as determined by annual US sales). They later consumed the drinks they had prepared in three blocks. Emotional valence was estimated using frontal asymmetry in EEG alpha band power and emotional arousal was estimated from EDA tonic and phasic activity. Results from 26 participants showed that the market-leading coffee machine was more efficient to use based on self-reports, behavioral performance measures, and there were significant within-subject differences in valence between the two machine use. Moreover, the market leader user interface led to greater self-reported product preference, which was further supported by significant differences in measured arousal and valence (EDA and EEG, respectively) during coffee production and consumption. This is the first study that uses a multimodal and comprehensive assessment of coffee machine use and beverage consumption in a naturalistic work environment. Approaches described in this study can be adapted in the future to other task-specific machine usability and consumer neuroscience studies.

A Deep-Learning Model for Subject-Independent Human Emotion Recognition Using Electrodermal Activity Sensors

One of the main objectives of Active and Assisted Living (AAL) environments is to ensure that elderly and/or disabled people perform/live well in their immediate environments; this can be monitored by among others the recognition of emotions based on non-highly intrusive sensors such as Electrodermal Activity (EDA) sensors. However, designing a learning system or building a machine-learning model to recognize human emotions while training the system on a specific group of persons and testing the system on a totally a new group of persons is still a serious challenge in the field, as it is possible that the second testing group of persons may have different emotion patterns. Accordingly, the purpose of this paper is to contribute to the field of human emotion recognition by proposing a Convolutional Neural Network (CNN) architecture which ensures promising robustness-related results for both subject-dependent and subject-independent human emotion recognition. The CNN model has been trained using a grid search technique which is a model hyperparameter optimization technique to fine-tune the parameters of the proposed CNN architecture. The overall concept’s performance is validated and stress-tested by using MAHNOB and DEAP datasets. The results demonstrate a promising robustness improvement regarding various evaluation metrics. We could increase the accuracy for subject-independent classification to 78% and 82% for MAHNOB and DEAP respectively and to 81% and 85% subject-dependent classification for MAHNOB and DEAP respectively (4 classes/labels). The work shows clearly that while using solely the non-intrusive EDA sensors a robust classification of human emotion is possible even without involving additional/other physiological signals.

Examining the Association Between Electrodermal Activity and Problem Behavior in Severe Autism Spectrum Disorder: A Feasibility Study

Background: Many individuals with autism spectrum disorder (ASD) engage in problem behavior, presenting significant challenges for those providing care and services for this population. Psychophysiological measures of arousal, such as electrodermal activity (EDA), may provide an early indication of subsequent problem behavior. However, variability in EDA patterns associated with behaviors may limit this predictive ability. Methods: EDA data was sampled from eight individuals with severe ASD in a naturalistic setting, while participating in educational programming in a school setting at a residential facility for severely affected individuals with developmental disabilities, to examine variability in EDA patterns. Results: An anticipatory rise in EDA only occurred 60% of the time prior to the problem behavior. Additionally, EDA after a problem behavior returned to median baseline levels only 45% of the time. Conclusions: Heterogeneity of EDA responses in those with the most severe forms of ASD will be an important consideration in future studies utilizing psychophysiological tools such as EDA to anticipate problem behavior, including the need for monitoring of return to baseline after problem behaviors. Incorporation of this consideration may lead to greater reliability of these approaches to help anticipate and manage problem behaviors.

An Open-Source Feature Extraction Tool for the Analysis of Peripheral Physiological Data

Electrocardiogram, electrodermal activity, electromyogram, continuous blood pressure, and impedance cardiography are among the most commonly used peripheral physiological signals (biosignals) in psychological studies and healthcare applications, including health tracking, sleep quality assessment, disease early-detection/diagnosis, and understanding human emotional and affective phenomena. This paper presents the development of a biosignal-specific processing toolbox (Bio-SP tool) for preprocessing and feature extraction of these physiological signals according to the state-of-the-art studies reported in the scientific literature and feedback received from the field experts. Our open-source Bio-SP tool is intended to assist researchers in affective computing, digital and mobile health, and telemedicine to extract relevant physiological patterns (i.e., features) from these biosignals semi-automatically and reliably. In this paper, we describe the successful algorithms used for signal-specific quality checking, artifact/noise filtering, and segmentation along with introducing features shown to be highly relevant to category discrimination in several healthcare applications (e.g., discriminating patterns associated with disease versus non-disease). Further, the Bio-SP tool is a publicly-available software written in MATLAB with a user-friendly graphical user interface (GUI), enabling future crowd-sourced modification to these tools. The GUI is compatible with MathWorks Classification Learner app for inference model development, such as model training, cross-validation scheme farming, and classification result computation.

Autonomic changes in psychogenic nonepileptic seizures: toward a potential diagnostic biomarker?

Disturbances of the autonomic nervous system (ANS) are common in neuropsychiatric disorders. Disease specific alterations of both sympathetic and parasympathetic activity can be assessed by heart rate variability (HRV), whereas electrodermal activity (EDA) can assess sympathetic activity. In posttraumatic stress disorder (PTSD), parasympathetic HRV parameters are typically decreased and EDA is increased, whereas in major depressive disorder (MDD) and dissociation, both parasympathetic and sympathetic markers are decreased. ANS abnormalities have also been identified in psychogenic nonepileptic seizures (PNES) by using HRV, indicating lower parasympathetic activity at baseline. In addition to reviewing the current literature on ANS abnormalities in PTSD, MDD, and disorders with prominent dissociation, including borderline personality disorder (BPD), this article also presents data from a pilot study on EDA in patients with PNES. Eleven patients with PNES, during an admission to our epilepsy monitoring unit (EMU), were compared with 9 with generalized tonic-clonic seizures (GTCS). The area under the EDA curve, the number of EDA responses lasting longer than 2 seconds, and the number of EDA surges during sleep (sympathetic sleep storms) were calculated on ictal and interictal days by an automated algorithm. EDA changes in PNES patients did not follow a systematic pattern of sympathetic hyperarousal (like EDA after GTCS) but were more variable. How specific PNES semiologies, and/or underlying neuropsychiatric disorders, may influence ictal and interictal EDA patterns, and lead to a novel diagnostic biomarker remains to be evaluated in future larger studies.