An Investigation on Bilateral Asymmetry in Electrodermal Activity

Lateral asymmetry of emotional arousal as a biomarker of attributional style during darts competitions

According to the Multiple Arousal Theory, electrodermal activity (EDA) is not uniform across the body. However, the psychological meaning of a left or right-sided EDA dominance is still not clear. We explored EDA lateral asymmetry as a psychophysiological marker of optimistic and pessimistic attributional style regarding success and failure in a darts competition. Bilateral EDA pattern of 230 throws of a competing pair was measured by Obimon EDA including accelerometer measurements of movements. First, we confirmed that lateral asymmetry can be measured reliably based on EDA data from both wrists. Second, we assessed attributional styles related to lateral asymmetry based on 80 individual throws. We recorded participants' expectations regarding their upcoming performance, and their attribution of success and failure based on Seligman's definition as optimist (internal cause attributed to success, or external cause ascribed to failure) or pessimist. The ratio of optimist and pessimist attributions was significantly different for throws with right or left-sided EDA dominance (p = 0.001). Optimistic attribution characterized 84% of right dominant, while pessimist 63% of left-dominant EDA during throws. We replicated these findings on 50 throws from 10 more individuals (p = 0.034). All individuals were right-handed. We conclude that wrist EDA can be reliably measured during physical movements, such as in a darts game. Lateral EDA asymmetry is a consistent psychophysiological marker of the attitude toward success and failure in a competitive setting, suggesting that lateral asymmetry of emotional arousal may serve as a novel psychophysiological biomarker for attribution style. Results underlie the psychophysiological relevance of bilateral arousal assessment and provide evidence-based verification for the Multiple Arousal Theory.

Selecting optimal wearables for measuring physiological arousal in robot-delivered mindfulness-based exercises

While social robots show promise for therapeutic interventions, accurate assessments of (vulnerable) participants' affective outcomes require attention. The careful selection of devices for recording autonomic processes in response to stress-inducing and relaxing exercises is essential to ensure data quality recordings and participants' comfort. This foundational study assessed two commonly utilized devices to record electrodermal activity (EDA), indexed through skin conductance, concerning their sensitivity to stress-relaxation manipulations and social validity during a robot intervention: a sock with a Shimmer device and a wrist-worn Empatica E4. We aimed to select the most sensitive and easy-to-wear one as a precursor to a larger intervention study featuring mindfulness-based relaxation exercises delivered by an NAO robot. The findings, based on 28 healthy Dutch-speaking adult volunteers wearing both devices, revealed sensitivity in detecting EDA variations in arousal following stressful (increase) and Robot-delivered mindfulness-based relaxation (decrease) exercises, further corroborated by self-reports. Bland-Altman results suggested little agreement between the two devices and lower sensitivity for the Empatica E4. No statistically significant differences concerning wearing comfort between the Empatica E4 and the Shimmer devices emerged. Although both devices independently showed sensitivity to stress/relaxation manipulation, the choice for one or the other should be informed by the activities in the intervention.

Lateralization of autonomic output in response to limb-specific threat

In times of stress or danger, the autonomic nervous system (ANS) signals the fight or flight response. A canonical function of ANS activity is to globally mobilize metabolic resources, preparing the organism to respond to threat. Yet a body of research has demonstrated that, rather than displaying a homogenous pattern across the body, autonomic responses to arousing events - as measured through changes in electrodermal activity (EDA) - can differ between right and left body locations. Surprisingly, an attempt to identify a function of ANS asymmetry consistent with its metabolic role has not been investigated. In the current study, we investigated whether asymmetric autonomic responses could be induced through limb-specific aversive stimulation. Participants were given mild electric stimulation to either the left or right arm while EDA was monitored bilaterally. In a group-level analyses, an ipsilateral EDA response bias was observed, with increased EDA response in the hand adjacent to the stimulation. This effect was observable in ∼50% of individual particpants. These results demonstrate that autonomic output is more complex than canonical interpretations suggest. We suggest that, in stressful situations, autonomic outputs can prepare either the whole-body fight or flight response, or a simply a limb-localized flick, which can effectively neutralize the threat while minimizing global resource consumption. These findings are consistent with recent theories proposing evolutionary leveraging of neural structures organized to mediate sensory responses for processing of cognitive emotional cues.Significance statementThe present study constitutes novel evidence for an autonomic nervous response specific to the side of the body exposed to direct threat. We identify a robust pattern of electrodermal response at the body location that directly receives aversive tactile stimulation. Thus, we demonstrate for the first time in contemporary research that the ANS is capable of location-specific outputs within single effector organs in response to small scale threat. This extends the canonical view of the role of ANS responses in stressful or dangerous stresses - that of provoking a 'fight or flight' response - suggesting a further role of this system: preparation of targeted limb-specific action, i.e., a flick.

Current trends and opportunities in the methodology of electrodermal activity measurement

Electrodermal activity (EDA) has been measured in the laboratory since the late 1800s. Although the influence of sudomotor nerve activity and the sympathetic nervous system on EDA is well established, the mechanisms underlying EDA signal generation are not completely understood. Owing to simplicity of instrumentation and modern electronics, these measurements have recently seen a transfer from the laboratory to wearable devices, sparking numerous novel applications while bringing along both challenges and new opportunities. In addition to developments in electronics and miniaturization, current trends in material technology and manufacturing have sparked innovations in electrode technologies, and trends in data science such as machine learning and sensor fusion are expanding the ways that measurement data can be processed and utilized. Although challenges remain for the quality of wearable EDA measurement, ongoing research and developments may shorten the quality gap between wearable EDA and standardized recordings in the laboratory. In this topical review, we provide an overview of the basics of EDA measurement, discuss the challenges and opportunities of wearable EDA, and review recent developments in instrumentation, material technology, signal processing, modeling and data science tools that may advance the field of EDA research and applications over the coming years.

Correlation Analysis of Different Measurement Places of Galvanic Skin Response in Test Groups Facing Pleasant and Unpleasant Stimuli

The galvanic skin response (GSR; also widely known as electrodermal activity (EDA)) is a signal for stress-related studies. Given the sparsity of studies related to the GSR and the variety of devices, this study was conducted at the Human Health Activity Laboratory (H2AL) with 17 healthy subjects to determine the variability in the detection of changes in the galvanic skin response among a test group with heterogeneous respondents facing pleasant and unpleasant stimuli, correlating the GSR biosignals measured from different body sites. We experimented with the right and left wrist, left fingers, the inner side of the right foot using Shimmer3GSR and Empatica E4 sensors. The results indicated the most promising homogeneous places for measuring the GSR, namely, the left fingers and right foot. The results also suggested that due to a significantly strong correlation among the inner side of the right foot and the left fingers, as well as the moderate correlations with the right and left wrists, the foot may be a suitable place to homogenously measure a GSR signal in a test group. We also discuss some possible causes of weak and negative correlations from anomalies detected in the raw data possibly related to the sensors or the test group, which may be considered to develop robust emotion detection systems based on GRS biosignals.

Evaluation of the Visual Stimuli on Personal Thermal Comfort Perception in Real and Virtual Environments Using Machine Learning Approaches

Personal Thermal Comfort models consider personal user feedback as a target value. The growing development of integrated “smart” devices following the concept of the Internet of Things and data-processing algorithms based on Machine Learning techniques allows developing promising frameworks to reach the best level of indoor thermal comfort closest to the real needs of users. The article investigates the potential of a new approach aiming at evaluating the effect of visual stimuli on personal thermal comfort perception through a comparison of 25 participants’ feedback exposed to a real scenario in a test cell and the same environment reproduced in Virtual Reality. The users’ biometric data and feedback about their thermal perception along with environmental parameters are collected in a dataset and managed with different Machine Learning techniques. The most suitable algorithm, among those selected, and the influential variables to predict the Personal Thermal Comfort Perception are identified. The Extra Trees classifier emerged as the most useful algorithm in this specific case. In real and virtual scenarios, the most important variables that allow predicting the target value are identified with an average accuracy higher than 0.99.