Electrodermal Activity Recordings Research Articles

Ambulatory autonomic nervous system activity in relation to hearing impairment.

Previous research has demonstrated that hearing impairment is associated with heightened subjective experiences of listening effort, fatigue, and stress, impacting daily functioning. This study aimed to evaluate whether hearing impairment alters physiological stress systems and whether different aspects of hearing impairment could vary in predicting dysregulation in these systems. Hallmark measures of parasympathetic and sympathetic nervous system activity were derived from electrocardiography, impedance cardiography, and electrodermal activity recordings taken from 133 individuals, aged 37 to 73, over two 24-hr periods, including sleep. Using ecological momentary assessment (EMA), participants reported mood, listening effort, and fatigue seven times daily. Hearing impairment was quantified through pure tone thresholds, speech perception in noise testing, and the Amsterdam Inventory for Auditory Disability questionnaire (Amsterdam Inventory). Using mixed models, we compared average daytime and sleep values of physiological measures across the 2 days, and their daytime-to-sleep contrast, based on each hearing impairment assessment. Results indicated that all three hearing impairment assessments were strong predictors of EMA outcomes of listening effort and fatigue. Contrary to expectations, hearing impairment did not have a significant impact on parasympathetic activity in daily life or on skin sympathetic activity. However, individuals with higher impairment exhibited a larger change in a cardiac sympathetic measure, the pre-ejection period, during wakefulness compared to sleep. Overall, hearing impairment had a small impact on autonomic nervous system functioning in daily life, but the effects were potentially attenuated by reduced exposure to listening demand in those with hearing impairment.

Autonomous nervous system responses to environmental-level exposure to 5G's first deployed band (3.5GHz) in healthy human volunteers.

Following the global progressive deployment of 5G networks, considerable attention has focused on assessing their potential impact on human health. This study aims to investigate autonomous nervous system changes by exploring skin temperature and electrodermal activity (EDA) among 44 healthy young individuals of both sexes during and after exposure to 3.5GHz antenna-emitted signals, with an electrical field intensity ranging from 1 to 2V/m. The study employed a randomized, cross-over design with triple-blinding, encompassing both 'real' and 'sham' exposure sessions, separated by a maximum interval of 1week. Each session comprised baseline, exposure and postexposure phases, resulting in the acquisition of seven runs. Each run initiated with a 150s segment of EDA recordings stimulated by 10 repeated beeps. Subsequently, the collected data underwent continuous decomposition analysis, generating specific indicators assessed alongside standard metrics such as trough-to-peak measurements, global skin conductance and maximum positive peak deflection. Additionally, non-invasive, real-time skin temperature measurements were conducted to evaluate specific anatomical points (hand, head and neck). The study suggests that exposure to 3.5GHz signals may potentially affect head and neck temperature, indicating a slight increase in this parameter. Furthermore, there was a minimal modulation of certain electrodermal metrics after the exposure, suggesting a potentially faster physiological response to auditory stimulation. However, while the results are significant, they remain within the normal physiological range and could be a consequence of an uncontrolled variable. Given the preliminary nature of this pilot study, further research is needed to confirm the effects of 5G exposure.

EDA-Graph: Graph Signal Processing of Electrodermal Activity for Emotional States Detection.

The continuous detection of emotional states has many applications in mental health, marketing, human-computer interaction, and assistive robotics. Electrodermal activity (EDA), a signal modulated by sympathetic nervous system activity, provides continuous insight into emotional states. However, EDA possesses intricate nonstationary and nonlinear characteristics, making the extraction of emotion-relevant information challenging. We propose a novel graph signal processing (GSP) approach to model EDA signals as graphical networks, termed EDA-graph. The GSP leverages graph theory concepts to capture complex relationships in time-series data. To test the usefulness of EDA-graphs to detect emotions, we processed EDA recordings from the CASE emotion dataset using GSP by quantizing and linking values based on the Euclidean distance between the nearest neighbors. From these EDA-graphs, we computed the features of graph analysis, including total load centrality (TLC), total harmonic centrality (THC), number of cliques (GNC), diameter, and graph radius, and compared those features with features obtained using traditional EDA processing techniques. EDA-graph features encompassing TLC, THC, GNC, diameter, and radius demonstrated significant differences (p < 0.05) between five emotional states (Neutral, Amused, Bored, Relaxed, and Scared). Using machine learning models for classifying emotional states evaluated using leave-one-subject-out cross-validation, we achieved a five-class F1 score of up to 0.68.

Changes in electrodermal activity following sympathicotomy in hyperhidrosis patients.

The aim of this study was to assess the potential of electrodermal activity (EDA) as a diagnostic tool for preoperative evaluation in hyperhidrosis patients. EDA levels and patterns in different skin areas were investigated before and after endoscopic thoracic sympathicotomy (ETS) and was compared to healthy subjects. Thirty-seven patients underwent two days of measurements before and after the operation. Twenty-five (67.5%) of the patients also had a third measurement after six months. Non-invasive EDA measurements, involving skin conductance, were sampled from five different skin areas while patients were at rest in supine and sitting positions or when subjected to stimuli such as deep inspirations, mental challenge, and exposure to a sudden loud sound. Prior to the operation, hyperhidrosis patients showed higher spontaneous palm EDA variations at rest and stronger responses to stimuli compared to healthy subjects. Patients with facial blushing/hyperhidrosis or combined facial/palmar hyperhidrosis showed minimal spontaneous activity or responses, particularly during mental challenge and sound stimulus. Notably, palm EDA response was abolished shortly following sympathicotomy, although a minor response was observed after six months. Minimal EDA responses were also observed in the back and abdomen postoperatively. Hyperhidrosis patients showed stronger EDA response to stimuli compared to healthy subjects. Sympathicotomy resulted in the complete elimination of palm EDA responses, gradually returning to a limited extent after six months. These findings suggest that EDA recordings could be utilized in preoperative assessment of hyperhidrosis patients.

Electrodermal activity: A continuous monitor of well-being

A smart society should not only support the efficiency and effectiveness of processes, they should also have the ability to comprehend and support its inhabitants well-being. To enable this, we need continuous information on individual’s well-being. As a first step, we suggest to evaluate people’s long term stress level, as it sheds light on their well-being. We propose using the biosignal Electrodermal Activity (EDA), a proven measure of people’s arousal or stress. Moreover, EDA can be measured continuously, in real-time, and unobtrusively via wearables. To assess this idea’s feasibility, the CASE, CEAP-360VR, and K-EmoCon datasets, including both continuous EDA recordings and continuous subjective self-reported arousal, were analyzed. With CASE and CEAP-360VR, a multilevel analysis unveiled that the frequency and amplitude of EDA’s non-specific skin conductance responses significantly correlated with the self-reported arousal. With K-EmoCon, this correlation was absent, which can be explained by the low arousal condition of this dataset. The latter also signals a main limitation: the lack of sensitivity of the proposed signal. Nevertheless, the results are promising as they show potential for EDA as a continuous stress monitor. Therefore, we are optimistic that a future smart society can be enabled to monitor and improve their inhabitant’s well-being.

Mutual Information between EDA and EEG in Multiple Cognitive Tasks and Sleep Deprivation Conditions.

Sleep deprivation, a widespread phenomenon that affects one-third of normal American adults, induces adverse changes in physical and cognitive performance, which in turn increases the occurrence of accidents. Sleep deprivation is known to increase resting blood pressure and decrease muscle sympathetic nerve activity. Monitoring changes in the interplay between the central and autonomic sympathetic nervous system can be a potential indicator of human's readiness to perform tasks that involve a certain level of cognitive load (e.g., driving). The electroencephalogram (EEG) is the standard to assess the brain's activity. The electrodermal activity (EDA) is a reflection of the general state of arousal regulated by the activation of the sympathetic nervous system through sweat gland stimulation. In this work, we calculated the mutual information between EDA and EEG recordings in order to consider linear and non-linear interactions and provide an insight of the relationship between brain activity and peripheral autonomic sympathetic activity. We analyzed EEG and EDA data from ten participants performing four cognitive tasks every two hours during 24 h (12 trials). We decomposed EEG data into delta, theta, alpha, beta, and gamma spectral components, and EDA into tonic and phasic components. The results demonstrate high values of mutual information between the EDA and delta component of EEG, mainly in working memory tasks. Additionally, we found an increase in the theta component of EEG in the presence of fatigue caused by sleep deprivation, the alpha component in tasks demanding inhibition and attention, and the delta component in working memory tasks. In terms of the location of brain activity, most of the tasks report high mutual information in frontal regions in the initial trials, with a trend to decrease and become uniform for all the nine analyzed EEG channels as a consequence of the sleep deprivation effect. Our results evidence the interplay between central and sympathetic nervous activity and can be used to mitigate the consequences of sleep deprivation.

Behavioral, cortical and autonomic effects of single-dose escitalopram on the induction and regulation of fear and disgust: Comparison with single-session psychological emotion regulation with reappraisal.

Adaptive and successful emotion regulation, the ability to flexibly exert voluntary control over emotional experience and the ensuing behavior, is vital for optimal daily functioning and good mental health. In clinical settings, pharmacological and psychological interventions are widely employed to modify pathological emotion processing and ameliorate its deleterious consequences. In this study, we investigated the acute effects of single-dose escitalopram on the induction and regulation of fear and disgust in healthy subjects. Furthermore, we compared these pharmacological effects with psychological emotion regulation that utilized a cognitive strategy with reappraisal. Emotion induction and regulation tasks were performed before and 4 h after ingestion of placebo or 10 mg escitalopram in a randomized, double-blind design. The International Affective Picture System (IAPS) was used as a source of images, with threat-related pictures selected for fear and disease and contamination-related pictures for disgust. Behavioral data, electrodermal activity (EDA), and functional near-infrared spectroscopy (fNIRS) recordings were collected. Escitalopram significantly reduced emotion intensity for both fear and disgust during emotion induction, albeit with differing electrodermal and hemodynamic activity patterns for the two negative emotions. At rest, i.e., in the absence of emotive stimuli, escitalopram increased sympathetic activity during the fear but not during the disgust experiments. For both fear and disgust, emotion regulation with reappraisal was more effective in reducing emotion intensity compared to pharmacological intervention with escitalopram or placebo. We concluded that emotion regulation with reappraisal and acute administration of escitalopram, but not placebo, reduce emotion intensity for both fear and disgust, with cognitive regulation being significantly more efficient compared to pharmacological regulation under the conditions of this study. Results from the fNIRS and EDA recordings support the concept of differential mechanisms of emotion regulation that could be emotion-specific.

Characterizing player responses to surprising events in 2D platform games

This paper presents an analysis of how players perceive and react to surprising events in a 2D platform game, Spelunky. We present the results of a 34-participant study that focuses on investigating players’ emotional responses to generated events in the game that are designed to evoke a specific emotional response (surprise) from players. Facial recordings, player responses, electrodermal activity (EDA), and interbeat interval (IBI) recordings are correlated to explain the divergence in the perception of surprise across different players. We found that many of the self-reported surprising events did not elicit any physical response from players, and EDA and IBI recordings collected using Empatica E4 could detect only 56% of reactions. However, data aggregated from chosen sources was able to capture 82% of all self-reported surprising events advocating the case of an automatic surprising detection framework for video games. We propose a framework for categorizing surprising events into three distinct categories and show that surprise can have varied responses from a player based on prior gaming experience, which had a negative correlation with the number of perceived surprises. Additionally, we provide suggestions on how to extend the existing theory of surprise in games toward automated game design.

Influence of ambient temperature on tonic and phasic electrodermal activity components

Objective. Electrodermal activity (EDA) is a reliable indicator for variations in the skin electrical properties attributed to sympathetic nerve system activity. EDA recordings can be influenced by various internal and external factors including environmental ones. Ambient temperature can be considered as one of the possible factors which might influence EDA recordings. Hence, this study aimed to precisely investigate influence of ambient temperature on tonic and phasic EDA components by employing a new EDA measurement technique, which depends on simultaneously recording of several EDA parameters. Approach. Tonic and phasic EDA components during three different ambient temperature levels were recorded from 36 healthy participants. In addition, for evoking electrodermal responses, participants were exposed to cognitive, visual and breathing external stimuli. Main results. Significant effects of temperature on tonic skin conductance (SC), skin susceptance (SS) and skin potential (SP) were obtained, whereas such significant effects were not observed with phasic SC, SS and SP. Tonic EDA parameters were increased as a function of temperature, but changes in phasic component were fluctuating. Significance. This should mean that, keeping recording of tonic EDA component in normal room temperature is highly crucial, but for recording or analysis of phasic component it is not important as they are more robust in this context. This is important in applications of EDA instruments, particularly in wearable devices where environmental temperature typically cannot be controlled.

Seizures Caused by Exposure to Hyperbaric Oxygen in Rats Can Be Predicted by Early Changes in Electrodermal Activity.

Hyperbaric oxygen (HBO2) is breathed during undersea operations and in hyperbaric medicine. However, breathing HBO2 by divers and patients increases the risk of central nervous system oxygen toxicity (CNS-OT), which ultimately manifests as sympathetic stimulation producing tachycardia and hypertension, hyperventilation, and ultimately generalized seizures and cardiogenic pulmonary edema. In this study, we have tested the hypothesis that changes in electrodermal activity (EDA), a measure of sympathetic nervous system activation, precedes seizures in rats breathing 5 atmospheres absolute (ATA) HBO2. Radio telemetry and a rodent tether apparatus were adapted for use inside a sealed hyperbaric chamber. The tethered rat was free to move inside a ventilated animal chamber that was flushed with air or 100% O2. The animal chamber and hyperbaric chamber (air) were pressurized in parallel at ~1 atmosphere/min. EDA activity was recorded simultaneously with cortical electroencephalogram (EEG) activity, core body temperature, and ambient pressure. We have captured the dynamics of EDA using time-varying spectral analysis of raw EDA (TVSymp), previously developed as a tool for sympathetic tone assessment in humans, adjusted to detect the dynamic changes of EDA in rats that occur prior to onset of CNS-OT seizures. The results show that a significant increase in the amplitude of TVSymp values derived from EDA recordings occurs on average (±SD) 1.9 ± 1.6 min before HBO2-induced seizures. These results, if corroborated in humans, support the use of changes in TVSymp activity as an early “physio-marker” of impending and potentially fatal seizures in divers and patients.

Time-varying Spectral Index of Electrodermal Activity to Predict Central Nervous System Oxygen Toxicity Symptoms in Divers: Preliminary results.

The most effective method to mitigate decompression sickness in divers is hyperbaric oxygen (HBO2) pre-breathing. However, divers breathing HBO2 are at risk for developing central nervous system oxygen toxicity (CNS-OT), which can manifest as symptoms that might impair a diver's performance, or cause more serious symptoms like seizures. In this study, we have collected electrodermal activity (EDA) signals in fifteen subjects at elevated oxygen partial pressures (2.06 ATA, 35 FSW) in the "foxtrot" chamber pool at the Duke University Hyperbaric Center, while performing a cognitive stress test for up to 120 minutes. Specifically, we have computed the time-varying spectral analysis of EDA (TVSymp) as a tool for sympathetic tone assessment and evaluated its feasibility for the prediction of symptoms of CNS-OT in divers. The preliminary results show large increase in the amplitude TVSymp values derived from EDA recordings ~2 minutes prior to expert human adjudication of symptoms related to oxygen toxicity. An early detection based on TVSymp might allow the diver to take countermeasures against the dire consequences of CNS-OT which can lead to drowning.Clinical Relevance-This study provides a sensitive analysis method which indicates a significant increase in the electrodermal activity prior to human expert adjudication of symptoms related to CNS-OT.

A Preliminary Study on Automatic Motion Artifact Detection in Electrodermal Activity Data Using Machine Learning.

The electrodermal activity (EDA) signal is a sensitive and non-invasive surrogate measure of sympathetic function. Use of EDA has increased in popularity in recent years for such applications as emotion and stress recognition; assessment of pain, fatigue, and sleepiness; diagnosis of depression and epilepsy; and other uses. Recently, there have been several studies using ambulatory EDA recordings, which are often quite useful for analysis of many physiological conditions. Because ambulatory monitoring uses wearable devices, EDA signals are often affected by noise and motion artifacts. An automated noise and motion artifact detection algorithm is therefore of utmost importance for accurate analysis and evaluation of EDA signals. In this paper, we present machine learning-based algorithms for motion artifact detection in EDA signals. With ten subjects, we collected two simultaneous EDA signals from the right and left hands, while instructing the subjects to move only the right hand. Using these data, we proposed a cross-correlation-based approach for non-biased labeling of EDA data segments. A set of statistical, spectral and model-based features were calculated which were then subjected to a feature selection algorithm. Finally, we trained and validated several machine learning methods using a leave-one-subject-out approach. The classification accuracy of the developed model was 83.85% with a standard deviation of 4.91%, which was better than a recent standard method that we considered for comparison to our algorithm.

Twenty-four-hour patterns in electrodermal activity recordings of patients with and without epileptic seizures.

Daytime and nighttime patterns affect the dynamic modulation of brain and body functions and influence the autonomic nervous system response to seizures. Therefore, we aimed to evaluate 24-hour patterns of electrodermal activity (EDA) in patients with and without seizures. We included pediatric patients with (a) seizures (SZ), including focal impaired awareness seizures (FIAS) or generalized tonic-clonic seizures (GTCS), (b) no seizures and normal electroencephalography (NEEG), or (c) no seizures but epileptiform activity in the EEG (EA) during vEEG monitoring. Patients wore a device that continuously recorded EDA and temperature (TEMP). EDA levels, EDA spectral power, and TEMP levels were analyzed. To investigate 24-hour patterns, we performed a nonlinear mixed-effects model analysis. Relative mean pre-ictal (-30min to seizure onset) and post-ictal (I: 30min after seizure offset; II: 30 to 60min after seizure offset) values were compared for SZ subgroups. We included 119 patients (40 SZ, 17 NEEG, 62 EA). EDA level and power group-specific models (SZ, NEEG, EA) (h=1; P<.01) were superior to the all-patient cohort model. Fifty-nine seizures were analyzed. Pre-ictal EDA values were lower than respective 24-hour modulated SZ group values. Post hoc comparisons following the period-by-seizure type interaction (EDA level: =18.50; P<.001, and power: =6.73; P=.035) revealed that EDA levels were higher in the post-ictal period I for FIAS and GTCS and in post-ictal period II for GTCS only compared to the pre-ictal period. Continuously monitored EDA shows a pattern of change over 24 hours. Curve amplitudes in patients with recorded seizures were lower as compared to patients who did not exhibit seizures during the recording period. Sympathetic skin responses were greater and more prolonged in GTCS compared to FIAS. EDA recordings from wearable devices offer a noninvasive tool to continuously monitor sympathetic activity with potential applications for seizure detection, prediction, and potentially sudden unexpected death in epilepsy (SUDEP) risk estimation.

Wireless Measurement of Sympathetic Arousal During in vivo Occupational Therapy Sessions.

PurposeOne goal of occupational therapists working with children who have sensory processing challenges is the regulation of arousal. Regulation strategies have not been evaluated using an empirical measure of physiological arousal.ObjectiveTo establish the feasibility of using an objective physiologic measure of sympathetic arousal in therapeutic settings and explore the relation between therapeutic activities and sympathetic arousal. To evaluate changes in electrodermal activity (EDA) during occupational therapy sessions.MethodsTwenty-two children identified with sensory modulation dysfunction (SMD) wore a wireless EDA sensor during 50 min occupational therapy sessions (n = 77 sessions).ResultsAll children were able to wear the sensor on the lower calf without being distracted by the device. The five insights below are based on a comparison of EDA recordings in relation to therapists’ reflections describing how sympathetic arousal might correspond to therapeutic activities.ConclusionObjective physiological assessment of a child’s sympathetic arousal during therapy is possible using a wireless EDA measurement system. Changes in EDA may correspond directly with therapeutic activities. The article provides a foundation for designing future therapeutic studies that include continuous measures of EDA.

Gender Differences in Tonic and Phasic Electrodermal Activity Components

Electrodermal activity (EDA) is a well-established psychophysiological measurement for research and clinical approaches. Males and females often display different physiological responses to stimuli, which can be detected by EDA recordings. Using a new method to measure skin conductance (SC), skin potential (SP) and skin susceptance (SS) simultaneously at the same electrode, these differences were investigated. SC, SP, and SS were recorded from 60 participants during relaxation and stress. It was found that both tonic and phasic EDA parameters indicated gender differences. In addition, females displayed greater tonic and phasic EDA parameters (except for skin potential responses (SPRs)) than males under both relaxation and conditions of stimulation (stress). However, these results were not statistically significant (p &gt; 0.05). This suggests that it is perhaps important to consider gender or at least note type of gender in EDA researches, but this cannot be generalized to clinical approaches.

Audio subtitling: dubbing and voice-over effects and their impact on user experience

ABSTRACT This article reports the outcome of an experiment in which 42 Spanish blind and partially sighted participants were exposed to two diverging audio subtitling strategies: audio subtitles with a voice-over effect and audio subtitles with a dubbing effect. Data on the users’ emotional responses were collected through a tactile and simplified version of the SAM questionnaire and psychophysiological recordings of electrodermal activity and heart rate. The results obtained from both methods do not show statistically significant differences between the two effects. However, results from the questionnaire proved that emotions were induced in the participants calling for more research on the topic and with the application of such methods.

Sleep preserves subjective and sympathetic emotional response of memories.

Sleep consolidates episodic content of emotional memories. Whether it likewise preserves or, to the contrary, depotentiates the emotional response associated with memory content is unclear, as there is conflicting evidence. In the current study, we investigated the influence of an afternoon nap (2-hr nap opportunity) on emotional responses of memories using multiple simultaneous measures. Young adults viewed 45 negative and 45 neutral pictures before taking a nap (measured with polysomnography) or remaining awake. Following the nap or wake period, participants viewed the same pictures intermixed with novel ones and indicated whether they remembered each picture. Emotional response to each picture was measured at both time points both subjectively, with valence and arousal ratings, and objectively, with recordings of electrodermal activity, electrocardiography, and corrugator supercilii electromyography. Compared to waking, a nap led to preserved subjective valence for negative pictures and preserved/increased skin conductance response in general. On the other hand, heart rate deceleration response decreased over the nap compared to wake interval, and this result was not influenced by picture type. These data suggest that sleep consolidates aspects of both subjective and physiological emotional response associated with episodic memory content. While sympathetic response appears to be preserved over sleep, parasympathetic response may be diminished.

A wavelet-based approach to emotion classification using EDA signals

Emotion is an intense mental experience often manifested by rapid heartbeat, breathing, sweating, and facial expressions. Emotion recognition from these physiological signals is a challenging problem with interesting applications such as developing wearable assistive devices and smart human-computer interfaces. This paper presents an automated method for emotion classification in children using electrodermal activity (EDA) signals. The time-frequency analysis of the acquired raw EDAs provides a feature space based on which different emotions can be recognized. To this end, the complex Morlet (C-Morlet) wavelet function is applied on the recorded EDA signals. The dataset used in this paper includes a set of multimodal recordings of social and communicative behavior as well as EDA recordings of 100 children younger than 30 months old. The dataset is annotated by two experts to extract the time sequence corresponding to three main emotions including “Joy”, “Boredom”, and “Acceptance”. The annotation process is performed considering the synchronicity between the children's facial expressions and the EDA time sequences. Various experiments are conducted on the annotated EDA signals to classify emotions using a support vector machine (SVM) classifier. The quantitative results show that the emotion classification performance remarkably improves compared to other methods when the proposed wavelet-based features are used.

Efficient wavelet-based artifact removal for electrodermal activity in real-world applications

Abstract Online monitoring of electrodermal activity (EDA) may serve as an economical and explicit source of information about actual emotional state and engagement level of users during their interaction with information and communications technologies (ICT) applications in real-world situations. In such contexts, however, EDA signal is affected by motion artifacts that introduce noise in the signal and can make it unusable. As the scope of movement minimization during EDA data acquisition is limited, this scenario demands online methods for detection and correction of artifacts with low computational cost. We propose an efficient wavelet-based method for artifacts attenuation while minimizing distortions, using a stationary wavelet transform (SWT) modeling the wavelet coefficients as a Laplace distribution. The proposed method was tested on EDA recordings from publicly available driver dataset collected during real-world driving, and containing a high number of motion artifacts, and the results were compared to those of three state-of-the-art methods for EDA signal filtering. In addition, the proposed method was tested for the online filtering of EDA signals collected while 12 volunteers conducted tasks designed to elicit various stress states. The results evidenced that the prediction of arousal states can be significantly improved after motion artifacts removal, and that the proposed method outperforms existing approaches and it has a lower computational cost. Taken together, these results evidence the effectiveness of the proposed method for online EDA filtering in real world scenarios.

Arousal and Valence Recognition of Affective Sounds Based on Electrodermal Activity

Physiological sensors and interfaces for mental healthcare are becoming of great interest in research and commercial fields. Specifically, biomedical sensors and related ad hoc signal processing methods can be profitably used for supporting objective, psychological assessments. However, a simple system able to automatically classify the emotional state of a healthy subject is still missing. To overcome this important limitation, we here propose the use of convex optimization-based electrodermal activity (EDA) framework and clustering algorithms to automatically discern arousal and valence levels induced by affective sound stimuli. EDA recordings were gathered from 25 healthy volunteers, using only one EDA sensor to be placed on fingers. Standardized stimuli were chosen from the International Affective Digitized Sound System database, and grouped into four different levels of arousal (i.e., the levels of emotional intensity) and two levels of valence (i.e., how unpleasant/pleasant a sound can be perceived). Experimental results demonstrated that our system is able to achieve a recognition accuracy of 77.33% on the arousal dimension, and 84% on the valence dimension.