Skin Conductance Features Research Articles

Subjective Fear in Virtual Reality: A Linear Mixed-Effects Analysis of Skin Conductance

The investigation of the physiological and pathological processes involved in fear perception is complicated due to the difficulties in reliably eliciting and measuring the complex construct of fear. This study proposes a novel approach to induce and measure subjective fear and its physiological correlates combining virtual reality (VR) with a mixed-effects model based on skin conductance (SC). Specifically, we developed a new VR scenario applying specific guidelines derived from horror movies and video games. Such a VR environment was used to induce fear in eighteen volunteers in an experimental protocol, including two relaxation scenarios and a neutral virtual environment. The SC signal was acquired throughout the experiment, and after each virtual scenario, the emotional state and fear perception level were assessed using psychometric scales. We statistically evaluated the greatest sympathetic activation induced by the fearful scenario compared to the others, showing significant results for most SC-derived features. Finally, we developed a rigorous mixed-effects model to explain the perceived fear as a function of the SC features. Model-fitting results showed a significant relationship between the fear perception scores and a combination of features extracted from both fast- and slow-varying SC components, proposing a novel solution for a more objective fear assessment.

Machine Learning Techniques for Prediction of Stress-Related Mental Disorders: A Scoping Review

The goal of this paper is to review the literature on machine learning (ML) and big data applications for mental health, emphasizing current research and practical implementations. To explore the field of ML in mental health, we used a scoping review process. The literature identified application domains of detection and prediction of stress as a contributor to mental health disorders. We evaluated the articles and data on the mental health application, machine learning approach, type of data (sensor, survey, etc.), and type of sensors. Most studies extracted features before developing AI-based stress detection algorithms. Findings revealed that heart rate, heart rate variability, and skin conductance features are the key indicators of stress. Moreover, among AI stress-detection methods, Random Forest and Neural Networks show promising results.

Sensitivity of Electrodermal Activity Features for Driver Arousal Measurement in Cognitive Load: The Application in Automated Driving Systems

Driver’s under-arousal occurred in automated driving systems (ADS) impairs takeover safety. This study aims to determine electrodermal activity (EDA) features’ importance for driver’s arousal quantification. A car-following simulator study was conducted with participants concurrently executing four levels of cognitive tasks, triggering four levels of arousal. Participants’ skin conductance (SC) data were collected and decomposed into tonic (skin conductance level, SCL) and phasic (skin conductance response, SCR) components. Seventeen features extracted from SC, SCL and SCR were compared. As a result, SCR-relevant features showed higher significance and larger effect size than SC and SCL features in response to cognitive load, which suggests the phasic component dominates changes in EDA under varying cognitive load. Moreover, the SCR rate TTP.nSCRs, identified by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.03 ~\mu \text{S}$ </tex-math></inline-formula> thresholds, attained the largest effect size among all features for driver’s arousal measurement. A varying time windows (TW) analysis showed that TTP.nSCRs was the most suggested arousal metric when TW was over 20 s, whereas the sum of SCRs amplitudes TTP.AmpSum was preferred when TW was less than 20 s. For driver’s arousal quantification with multi-features, the top five suggested features were TTP.nSCRs, SC_Rate5, CDA.SCR (or CDA.ISCR), CDA.AmpSum, and TTP.AmpSum. Although male drivers showed higher values of EDA features than female drivers, the sensitivity of the proposed EDA features stands across gender and individuals. This study promotes an improved understanding of EDA changes in human cognitive process. The sensitive EDA features proposed could be used from uni- or multi-modalities in driver state management and takeover-safety prediction for ADS.

A novel Hot-Flash classification algorithm via multi-sensor features integration.

We aim to evaluate the feasibility and performance of a novel hot flash (HF) classification algorithm based on multisensor features integration using commercial wearable sensors. First, we processed feature sets from wrist-based multi-sensor data (photoplethysmography, motion, temperature, skin conductance and). Then, we classified (Decision Tree) physiological-recorded HFs (N=27) recorded from three menopause women, and we assessed the algorithm performance against gold-standard HF expert evaluation. The results indicated that while skin conductance features alone explain most of the variance (~65%) in HF classification, the multi-sensor approach achieved above 90% sensitivity at 95.6% specificity in HF classification and showed advantages under conditions of signal corruption and different biobehavioral states (sleep vs wake). The proposed new multi-sensor approach showed being promising in HF classification using common commercially-available wearable sensors and target locations.Clinical Relevance- The development of "user-centered" accurate, automatic detection systems for HFs can advance the measurement and treatment of HFs.

The correlations among the skin conductance features responding to physiological stress stimuli.

The skin conductance responses (SCRs) are a well-accepted indicator of physiological arousal for both research purposes and clinical approaches. The shape of SCRs is analyzed by various features. However, the estimation of how much (in %) one feature can explain another is still an open issue. The aim of this study was to assess whether variation in one SCR feature predicts changes in other features. Skin conductance (SC) was measured during relaxation and mental stress in 40 subjects. SCRs were induced by three external stimuli, which were deep breath, a mental arithmetic, task and a visual task. The findings of this study showed that about 55% (R2 =0.55) of the variation in the half recovery time (SCRs_rec 50%) can be explained by the rise time (SCRs_ris), whereas variation in amplitude of the skin conductance responses (SCRs_amp) and the skin conductance level (SCL) is independent and cannot be explained by the other features, as R2 values obtained from all analyses among these SCR features in average were lower 0.19. The study results suggest that the two timing phases (SCRs_rec and SCRs_ris) are not completely independent from each other, although they might be governed by different sweating mechanisms (secretion and reabsorption). However, SCRs_amp and SCL were independent. These findings can help in choosing the optimal set of features of an automated system for processing EDA, which reflect the alterations in the activation level generated during an emotional episode.

A simplified method for online extraction of skin conductance features: A pilot study on an immersive virtual-reality-based motor task.

There is a growing body of literature that recognizes the importance of Skin Conductance (SC) for assessing changes in emotional states, such as engagement to learning tasks, and its importance to estimate possible drawbacks affecting overall performance. To date, most of the commonly used methods for SC signal analysis, i.e. detecting its phasic and tonic components and thus extracting informative features, are either too simple and unreliable or too complex and thus inaccessible and inflexible, as well as unable to perform online analyses. The current work proposes a simplified but clear and effective algorithm based on a Machine State to search for expected behaviors in the well-defined morphology of the signal. Eleven (11) features were correctly extracted from 79 healthy subjects during an experimental setup for immersive virtual rehabilitation (balance study case). The method was also successfully applied as a tool to identify significant changes in the subjective psychophysiological response to different experimental conditions. These results point toward a potential role in virtual rehabilitation applications by getting real-time feedback in human-in-the-loop approaches.

A Marked Point Process Filtering Approach for Tracking Sympathetic Arousal From Skin Conductance

Human emotion represents a complex neural process within the brain. The ability to automatically recognize emotions from physiological signals has the potential to impact humanity in multiple ways through applications in human-machine interaction, remote health monitoring, smart living environments and entertainment. We present a marked point process-based Bayesian filtering approach to track sympathetic arousal from skin conductance features. The rate at which individual skin conductance responses (SCRs) occur and their respective amplitudes encode important information regarding an individual's psychological arousal level. We develop a state-space model relating a latent neuropsychological arousal state to the rate at which neural impulses underlying SCRs occur and the impulse amplitudes. We simultaneously estimate both arousal and the state-space model parameters within an expectation-maximization framework. We evaluate our method on both simulated and experimental data. Results on simulated data indicate the method's ability to accurately estimate an unobserved state from marked point process observations. The experimental data include studies involving mental stress artificially-induced in laboratory environments, real-world driver stress and Pavlovian fear conditioning. Results on experimental data outperform previous Bayesian filtering approaches in terms of a lower sensitivity to small impulses and avoiding overfitting. The filter is thus able to estimate emotional arousal from skin conductance features and is a promising approach for everyday emotion recognition applications.

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

Neuroanatomical structures within the cortical and sub-cortical brain regions process emotion and cause subsequent variations in signals such as skin conductance and electrocardiography. The signals often encode information in their continuous-valued amplitudes or waves as well as in their underlying impulsive events. We propose to track psychological arousal from this hybrid source of skin conductance information. We present a point process state-space method in tandem with Bayesian filtering for determining a continuous-valued arousal state from skin conductance measurements. To perform state estimation, we relate arousal to binary- and continuous-valued observations derived from the phasic and tonic parts of a skin conductance signal, and recover model parameters using expectation-maximization. We evaluate our model on both synthetic and two different experimental data sets. Stress was artificially induced in the first experimental data set and the second comprised of a fear conditioning experiment. Results on the first data set indicate high levels of arousal during exposure to cognitive stress and low arousal during relaxation. Plausible results are also obtained in the fear conditioning data set consistent with previous skin conductance studies in similar experimental contexts. The state-space approach-which does not rely on external classification labels-is able to continuously track an arousal level from skin conductance features. The method is a promising arousal estimation scheme utilizing only skin conductance. The approach could find applications in wearable monitoring and the study of neuropsychiatric conditions such as post-traumatic stress disorder.

Skin Conductance Response to Gradual-Increasing Experimental Pain.

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

Prediction of Freezing of Gait in Parkinson's From Physiological Wearables: An Exploratory Study.

Freezing of gait (FoG) is a common gait impairment among patients with advanced Parkinson's disease. FoG is associated with falls and negatively impacts the patient's quality of life. Wearable systems that detect FoG in real time have been developed to help patients resume walking by means of rhythmic cueing. Current methods focus on detection, which require FoG events to happen first, while their prediction opens the road to preemptive cueing, which might help subjects to avoid freeze altogether. We analyzed electrocardiography (ECG) and skin-conductance (SC) data from 11 subjects who experience FoG in daily life, and found statistically significant changes in ECG and SC data just before the FoG episodes, compared to normal walking. Based on these findings, we developed an anomaly-based algorithm for predicting gait freeze from relevant SC features. We were able to predict 71.3% from 184 FoG with an average of 4.2 s before a freeze episode happened. Our findings enable the possibility of wearable systems, which predict with few seconds before an upcoming FoG from SC, and start external cues to help the user avoid the gait freeze.

Combining sudomotor nerve impulse estimation with fMRI to investigate the central sympathetic response to nausea.

The skin conductance (SC) signal is one of the most important non-invasive indirect measures of autonomic outflow. Several mathematical models have been proposed in the literature to characterize specific SC features. In this work, we present a method for the estimation of central control of sudomotor nerve impulse (SMI) function using SC. The method is based on a differential formulation decomposed into two first order differential equations. We validate our estimation framework by applying it on an experimental protocol where eleven motion sickness-prone subjects were exposed to a nauseogenic visual stimulus while SC and fMRI signals were recorded. Our results show an expected significant increase in the mean amplitude of SMI peaks during the highest reported nausea, as well as a decreasing trend during recovery, which was not evident for skin conductance level. Importantly, SMI/fMRI analysis found a negative association between SMI and fMRI signal in orbitofrontal, dorsolateral prefrontal, and posterior insula cortices, consistent with previous studies correlating brain fMRI and microneurographic signals.

Subject-dependent biosignal features for increased accuracy in psychological stress detection

This paper presents novel subject-dependent biosignal features, with a view towards increasing the effectiveness of automatic psychological stress detection. The features proposed in this work focus on suppressing between-subject variability that typically appears in biosignals like the skin conductance (SC) and the electrocardiogram (ECG), and degrades the performance of relevant emotion recognition (ER) systems. For this purpose, the proposed features employ filtering of input signals, on the basis of “rest signatures” calculated from each subject's baseline recordings. These signatures are biosignal transformations capable to express each individual's baseline deviation from signal templates, which would ideally be applied during rest. The proposed subject-dependent features, extracted from SC and ECG modalities, were found capable to significantly increase automatic stress detection accuracy over a multi-subject (N=24) data set, collected through an experiment of natural stress induction. They provided accuracy at the level of 95%, significantly improved to the respective result (86.05%) taken from common SC and ECG features that have been typically used in the past. They appeared also similarly effective in automatic frustration detection over a further dataset. The results of the present work indicate that the proposed subject-dependent features, can lead to significant advances in the performance of future relevant ER systems.