Assessment of Valance Emotional State Using EEG-EDA Coupling and Explainable Classifiers.

Emotion influences human life and impacts daily life activities. During emotional processes, physiological signals interact with each other instead of functioning separately. Although unimodal and multimodal approaches have been explored for emotion classification, there is a lack of inclusion of central and peripheral nervous system signal interaction-based approaches. In this study, an attempt has been made to characterize valance emotional states using Electroencephalogram (EEG)- Electrodermal activity (EDA) based coupling. For this, multimodal signals are obtained from the publicly available DEAP database (n=32 subjects). The EEG signals are decomposed into θ, α, ß, and bands and EDA signals are decomposed into phasic and tonic components. Then two EEG, three EDA, and two EEG-EDA coupling-based features are extracted and applied to three classifiers namely Random Forest (RF), Linear discriminant analysis, and Adaptive boosting. In addition, SHAP analysis is performed to explain classifiers' performance with respect to features. The result shows that the proposed approach is able to classify valence emotional states. The feature combination of EEG, EDA, and EEG-EDA coupling-based features with an RF classifier performs best with an F1-score of 68.21%. SHAP analysis in frontal electrodes with γ band obtained better discrimination among different valance states. This study underscores the significance of the coupling studies of EEG with EDA in classifying emotion. Therefore, the proposed approach can be extended to emotional state assessment in clinical settings.

Pupil contagion variation with gaze, arousal, and autistic traits.

Pupillary contagion occurs when one's pupil size unconsciously adapts to the pupil size of an observed individual and is presumed to reflect the transfer of arousal. Importantly, when estimating pupil contagion, low level stimuli properties need to be controlled for, to ensure that observations of pupillary changes are due to internal change in arousal rather than the external differences between stimuli. Here, naturalistic images of children's faces depicting either small or large pupils were presented to a group of children and adolescents with a wide range of autistic traits, a third of whom had been diagnosed with autism. We examined the extent to which pupillary contagion reflects autonomic nervous system reaction through pupil size change, heart rate and skin conductance response. Our second aim was to determine the association between arousal reaction to stimuli and degree of autistic traits. Results show that pupil contagion and concomitant heart rate change, but not skin conductance change, was evident when gaze was restricted to the eye region of face stimuli. A positive association was also observed between pupillary contagion and autistic traits when participants' gaze was constrained to the eye region. Findings add to a broader understanding of the mechanisms underlying pupillary contagion and its association with autism.

Machine learning-enabled detection of attention-deficit/hyperactivity disorder with multimodal physiological data: a case-control study.

BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental psychiatric condition that typically emerges during childhood but often persists into adulthood, significantly impacting individuals' functioning, relationships, productivity, and overall quality of life. However, the current diagnostic process exhibits limitations that can significantly affect its overall effectiveness. Notably, its face-to-face and time-consuming nature, coupled with the reliance on subjective recall of historical information and clinician subjectivity, stand out as key challenges. To address these limitations, objective measures such as neuropsychological evaluations, imaging techniques and physiological monitoring of the Autonomic Nervous System functioning, have been explored. METHODS: The main aim of this study was to investigate whether physiological data (i.e., Electrodermal Activity, Heart Rate Variability, and Skin Temperature) can serve as meaningful indicators of ADHD, evaluating its utility in distinguishing adult ADHD patients. This observational, case-control study included a total of 76 adult participants (32 ADHD patients and 44 healthy controls) who underwent a series of Stroop tests, while their physiological data was passively collected using a multi-sensor wearable device. Univariate feature analysis was employed to identify the tests that triggered significant signal responses, while the Informative k-Nearest Neighbors (KNN) algorithm was used to filter out less informative data points. Finally, a machine-learning decision pipeline incorporating various classification algorithms, including Logistic Regression, KNN, Random Forests, and Support Vector Machines (SVM), was utilized for ADHD patient detection. RESULTS: Results indicate that the SVM-based model yielded the optimal performance, achieving 81.6% accuracy, maintaining a balance between the experimental and control groups, with sensitivity and specificity of 81.4% and 81.9%, respectively. Additionally, integration of data from all physiological signals yielded the best results, suggesting that each modality captures unique aspects of ADHD. CONCLUSIONS: This study underscores the potential of physiological signals as valuable diagnostic indicators of adult ADHD. For the first time, to the best of our knowledge, our findings demonstrate that multimodal physiological data collected via wearable devices can complement traditional diagnostic approaches. Further research is warranted to explore the clinical applications and long-term implications of utilizing physiological markers in ADHD diagnosis and management.

Habituation of Central and Electrodermal Responses to an Auditory Two-Stimulus Oddball Paradigm.

The orienting reaction (OR) towards a new stimulus is subject to habituation, i.e., progressively attenuates with stimulus repetition. The skin conductance responses (SCRs) are known to represent a reliable measure of OR at the peripheral level. Yet, it is still a matter of debate which of the P3 subcomponents is the most likely to represent the central counterpart of the OR. The aim of the present work was to study habituation, recovery, and dishabituation phenomena intrinsic to a two-stimulus auditory oddball paradigm, one of the most-used paradigms both in research and clinic, by simultaneously recording SCRs and P3 in twenty healthy volunteers. Our findings show that the target stimulus was capable of triggering a more marked OR, as indexed by both SCRs and P3, compared to the standard stimulus, that could be due to its affective saliency and relevance for task completion; the application of temporal principal components analysis (PCA) to the P3 complex allowed us to identify several subcomponents including both early and late P3a (eP3a; lP3a), P3b, novelty P3 (nP3), and both a positive and a negative Slow Wave (+SW; -SW). Particularly, lP3a and P3b subcomponents showed a similar behavior to that observed for SCRs , suggesting them as central counterparts of OR. Finally, the P3 evoked by the first standard stimulus after the target showed a significant dishabituation phenomenon which could represent a sign of the local stimulus change. However, it did not reach a sufficient level to trigger an SCR/OR since it did not represent a salient event in the context of the task.

Advancing the integration of biosignal-based automated pain assessment methods into a comprehensive model for addressing cancer pain.

BACKGROUND: Tailoring effective strategies for cancer pain management requires a careful analysis of multiple factors that influence pain phenomena and, ultimately, guide the therapy. While there is a wealth of research on automatic pain assessment (APA), its integration with clinical data remains inadequately explored. This study aimed to address the potential correlations between subjective and APA-derived objectives variables in a cohort of cancer patients. METHODS: A multidimensional statistical approach was employed. Demographic, clinical, and pain-related variables were examined. Objective measures included electrodermal activity (EDA) and electrocardiogram (ECG) signals. Sensitivity analysis, multiple factorial analysis (MFA), hierarchical clustering on principal components (HCPC), and multivariable regression were used for data analysis. RESULTS: The study analyzed data from 64 cancer patients. MFA revealed correlations between pain intensity, type, Eastern Cooperative Oncology Group Performance status (ECOG), opioids, and metastases. Clustering identified three distinct patient groups based on pain characteristics, treatments, and ECOG. Multivariable regression analysis showed associations between pain intensity, ECOG, type of breakthrough cancer pain, and opioid dosages. The analyses failed to find a correlation between subjective and objective pain variables. CONCLUSIONS: The reported pain perception is unrelated to the objective variables of APA. An in-depth investigation of APA is required to understand the variables to be studied, the operational modalities, and above all, strategies for appropriate integration with data obtained from self-reporting. TRIAL REGISTRATION: This study is registered with ClinicalTrials.gov, number (NCT04726228), registered 27 January 2021, https://classic. CLINICALTRIALS: gov/ct2/show/NCT04726228?term=nct04726228&draw=2&rank=1.

Normatively irrelevant disgust interferes with decision under uncertainty: Insights from the Iowa gambling task.

This study investigates whether a not informative, irrelevant emotional reaction of disgust interferes with decision-making under uncertainty. We manipulate the Iowa Gambling Task (IGT) by associating a disgust-eliciting image with selections from Disadvantageous/Bad decks (Congruent condition) or Advantageous/Good decks (Incongruent condition). A Control condition without manipulations is also included. Results indicate an increased probability of selecting from a Good deck as the task unfolds in all conditions. However, this effect is modulated by the experimental manipulation. Specifically, we detect a detrimental effect (i.e., a significant decrease in the intercept) of the disgust-eliciting image in Incongruent condition (vs. Control), but this effect is limited to the early stages of the task (i.e., first twenty trials). No differences in performance trends are detected between Congruent and Control conditions. Anticipatory Skin Conductance Response, heart rate, and pupil dilation are also assessed as indexes of anticipatory autonomic activation following the Somatic Marker Hypothesis, but no effects are shown for the first two indexes in any of the conditions. Only a decreasing trend is detected for pupil dilation as the task unfolds in Control and Incongruent conditions. Results are discussed in line with the "risk as feelings" framework, the Somatic Marker Hypothesis, and IGT literature.

Elevated unanticipated acoustic startle reactivity in dyslexia.

People with dyslexia, a neurodevelopmental disorder of reading, are highly attuned to the emotional world. Compared with their typically developing peers, children with dyslexia exhibit greater autonomic nervous system reactivity and facial behaviour to emotion- and empathy-inducing film clips. Affective symptoms, such as anxiety, are also more common in children with dyslexia than in those without. Here, we investigated whether the startle response, an automatic reaction that lies at the interface of emotion and reflex, is elevated in dyslexia. We measured facial behaviour, electrodermal reactivity (a sympathetic nervous system measure) and emotional experience in response to a 100 ms, 105 dB unanticipated acoustic startle task in 30 children with dyslexia and 20 comparison children without dyslexia (aged 7-13) who were matched on age, sex and nonverbal reasoning. Our results indicated that the children with dyslexia had greater total facial behaviour and electrodermal reactivity to the acoustic startle task than the children without dyslexia. Across the sample, greater electrodermal reactivity during the startle predicted greater parent-reported anxiety symptoms. These findings contribute to an emerging picture of heightened emotional reactivity in dyslexia and suggest accentuated sympathetic nervous system reactivity may contribute to the elevated anxiety that is often seen in this population.

Investigating adverse daily life effects following a psychosocial laboratory stress task, and the moderating role of Psychopathology.

Laboratory stress tasks are necessary to closely investigate the stress response in a controlled environment. However, to our knowledge, no study has tested whether participating in such tasks can pose any daily life adverse effect. Fifty-three healthy participants (46 women) took part in a laboratory session where stress was induced using a typical psychosocial stressor: the repeated Montreal Imaging Stress Task (rMIST). Average levels of negative affect (NA), heart rate (HR), root mean square of successive differences (RMSSD), and skin conductance level (SCL), as well as reactivity across all these parameters as measured with the experience sampling method (ESM) in the four days prior to the laboratory session were compared with the four days following the session. We also assessed whether vulnerability to psychopathology moderated these associations. Findings showed that the task did not pose any significant adverse effect on participants. However, there was an unexpected increase in average RMSSD and a decrease in average SCL pre- to post- task. In addition, more vulnerable individuals were more likely to experience an increase in average levels of NA in the days following the task compared to the days preceding it. Our findings suggest that laboratory stress tasks may pose a significant risk to more vulnerable individuals.

Psychopathy, pain, and pain empathy: A psychophysiological study.

The present study examined whether people higher in psychopathy experienced less self-reported and psychophysiological nociceptive pressure than people lower in psychopathy. We also examined whether psychopathy affects empathy for others' pain via self-reported and psychophysiological measures. Three hundred and sixty-nine students (18-78 years; M = 26, SD = 9.34) were screened for psychopathic traits using the Youth Psychopathy Inventory (YPI). Stratified sampling was used to recruit 49 adults residing in the highest (n = 23) and lowest (n = 26) 20% of the psychopathy spectrum. Using skin conductance response (SCR) and self-report responses, participants responded to individually adjusted intensities of pneumatic pressure and others' pain images and completed self-reported psychopathy and empathy measures (Triarchic Psychopathy Measure, TriPm; Interpersonal Reactivity Index, IRI). People higher in psychopathy self-reported feeling less nociceptive pressure compared to people lower in psychopathy, yet we did not find any differences in SCR to nociceptive pressure. However, when viewing other people in pain, the high psychopathy group displayed lower SCR and lower self-reported empathy compared to those lower in psychopathy. Our results suggest psychopathic traits relate to problems empathising with others' pain, as well as the perception of nociceptive pressure. We also show support for the theory of dual harm which has been receiving increasing attention. Consequently, psychopathy interventions should focus both on recognising and empathising with the pain of others.

Deep learning-based stress detection for daily life use using single-channel EEG and GSR in a virtual reality interview paradigm.

This research aims to establish a practical stress detection framework by integrating physiological indicators and deep learning techniques. Utilizing a virtual reality (VR) interview paradigm mirroring real-world scenarios, our focus is on classifying stress states through accessible single-channel electroencephalogram (EEG) and galvanic skin response (GSR) data. Thirty participants underwent stress-inducing VR interviews, with biosignals recorded for deep learning models. Five convolutional neural network (CNN) architectures and one Vision Transformer model, including a multiple-column structure combining EEG and GSR features, showed heightened predictive capabilities and an enhanced area under the receiver operating characteristic curve (AUROC) in stress prediction compared to single-column models. Our experimental protocol effectively elicited stress responses, observed through fluctuations in stress visual analogue scale (VAS), EEG, and GSR metrics. In the single-column architecture, ResNet-152 excelled with a GSR AUROC of 0.944 (±0.027), while the Vision Transformer performed well in EEG, achieving peak AUROC values of 0.886 (±0.069) respectively. Notably, the multiple-column structure, based on ResNet-50, achieved the highest AUROC value of 0.954 (±0.018) in stress classification. Through VR-based simulated interviews, our study induced social stress responses, leading to significant modifications in GSR and EEG measurements. Deep learning models precisely classified stress levels, with the multiple-column strategy demonstrating superiority. Additionally, discreetly placing single-channel EEG measurements behind the ear enhances the convenience and accuracy of stress detection in everyday situations.

Alpha tACS on Parieto-Occipital Cortex Mitigates Motion Sickness Based on Multiple Physiological Observation.

Approximately one third of the population is prone to motion sickness (MS), which is associated with the dysfunction in the integration of sensory inputs. Transcranial alternating current stimulation (tACS) has been widely used to modulate neurological functions by affecting neural oscillation. However, it has not been applied in the treatment of motion sickness. This study aims to investigate changes in brain oscillations during exposure to MS stimuli and to further explore the potential impact of tACS with the corresponding frequency and site on MS symptoms. A total of 19 subjects were recruited to be exposed to Coriolis stimuli to complete an inducing session. After that, they were randomly assigned to tACS stimulation group or sham stimulation group to complete a stimulation session. Electroencephalography (EEG), electrocardiogram, and galvanic skin response were recorded during the experiment. All the subjects suffering from obvious MS symptoms after inducing session were observed that alpha power of four channels of parieto-occipital lobe significantly decreased (P7: t =3.589, p <0.001; P8: t =2.667, p <0.05; O1: t =3.556, p <0.001; O2: t =2.667, p <0.05). Based on this, tACS group received the tACS stimulation at 10Hz from Oz to CPz. Compared to sham group, tACS stimulation significantly improved behavioral performance and entrained the alpha oscillation in individuals whose alpha power decrease during the inducing session. The findings show that parieto-occipital alpha oscillation plays a critical role in the integration of sensory inputs, and alpha tACS on parieto-occipital can become a potential method to mitigate MS symptoms.

Adult attachment assessed via the ASA and AAI: Empirical convergence and links with autonomic physiological responding during attachment assessments.

This study examined the empirical convergence of Attachment Script Assessment (ASA) deactivation, hyperactivation, and anomalous scripts with conceptually corresponding attachment patterns assessed via the Adult Attachment Interview (AAI), and the significance of ASA dimensions for autonomic physiological reactivity during adult attachment assessments. Young adults' (50% male; Mage = 19 years; 80% White/European American) ASA deactivation, hyperactivation, and anomalous content were significantly associated with AAI dismissing (r = .26-.38), preoccupied (r = .31-.35), and unresolved (r = .37) states of mind, respectively. ASA hyperactivation and anomalous content were associated with heightened RSA reactivity to the AAI and ASA, aligning with expectations that these attachment patterns capture the tendency to heighten expressions of negative, traumatic experiences. ASA deactivation was associated with smaller increases in electrodermal activity to the ASA-indicative of less sympathetic arousal-converging with the tendency of individuals higher in deactivation to avoid discussing attachment themes in the ASA.

Robots as Mental Health Coaches: A Study of Emotional Responses to Technology-Assisted Stress Management Tasks Using Physiological Signals.

The current study investigated the effectiveness of social robots in facilitating stress management interventions for university students by evaluating their physiological responses. We collected electroencephalogram (EEG) brain activity and Galvanic Skin Responses (GSRs) together with self-reported questionnaires from two groups of students who practiced a deep breathing exercise either with a social robot or a laptop. From GSR signals, we obtained the change in participants' arousal level throughout the intervention, and from the EEG signals, we extracted the change in their emotional valence using the neurometric of Frontal Alpha Asymmetry (FAA). While subjective perceptions of stress and user experience did not differ significantly between the two groups, the physiological signals revealed differences in their emotional responses as evaluated by the arousal-valence model. The Laptop group tended to show a decrease in arousal level which, in some cases, was accompanied by negative valence indicative of boredom or lack of interest. On the other hand, the Robot group displayed two patterns; some demonstrated a decrease in arousal with positive valence indicative of calmness and relaxation, and others showed an increase in arousal together with positive valence interpreted as excitement. These findings provide interesting insights into the impact of social robots as mental well-being coaches on students' emotions particularly in the presence of the novelty effect. Additionally, they provide evidence for the efficacy of physiological signals as an objective and reliable measure of user experience in HRI settings.

The impact of the left inferior frontal gyrus on fear extinction: A transcranial direct current stimulation study.

INTRODUCTION: Fear extinction is a fundamental component of exposure-based therapies for anxiety-related disorders. The renewal of fear in a different context after extinction highlights the importance of contextual factors. In this study, we aimed to investigate the causal role of the left inferior frontal gyrus (LiFG) in the context-dependency of fear extinction learning via administration of transcranial direct current stimulation (tDCS) over this area. METHODS: 180 healthy subjects were assigned to 9 groups: 3 tDCS conditions (anodal, cathodal, and sham) × 3 context combinations (AAA, ABA, and ABB). The fear conditioning/extinction task was conducted over three consecutive days: acquisition, extinction learning, and extinction recall. tDCS (2 mA, 10min) was administered during the extinction learning phase over the LiFG via a 4-electrode montage. Skin conductance response (SCR) data and self-report assessments were collected. RESULTS: During the extinction learning phase, groups with excitability-enhancing anodal tDCS showed a significantly higher fear response to the threat cues compared to cathodal and sham stimulation conditions, irrespective of contextual factors. This effect was stable until the extinction recall phase. Additionally, excitability-reducing cathodal tDCS caused a significant decrease of the response difference between the threat and safety cues during the extinction recall phase. The self-report assessments showed no significant differences between the conditions throughout the experiment. CONCLUSION: Independent of the context, excitability enhancement of the LiFG did impair fear extinction, and led to preservation of fear memory. In contrast, excitability reduction of this area enhanced fear extinction retention. These findings imply that the LiFG plays a role in the fear extinction network, which seems to be however context-independent.

Electrodermal Activity (EDA) Morphologies and Prediction of Engagement with Simple Moving Average Crossover: A Mixed-Method Study.

Electrodermal Activity (EDA), which primarily indicates arousal through sympathetic nervous system activity, serves as a tool to measure constructs like engagement, cognitive load, performance, and stress. Despite its potential, empirical studies have often yielded mixed results and found it of limited use. To better understand EDA, we conducted a mixed-methods study in which quantitative EDA profiles and survey data were investigated using qualitative interviews. This study furnishes an EDA dataset measuring the engagement levels of seven participants who watched three videos for 4-10 min. The subsequent interviews revealed five EDA morphologies with varying short-term signatures and long-term trends. We used this dataset to demonstrate the moving average crossover, a novel metric for EDA analysis, in predicting engagement-disengagement dynamics in such data. Our contributions include the creation of the detailed dataset, comprising EDA profiles annotated with qualitative data, the identification of five distinct EDA morphologies, and the proposition of the moving average crossover as an indicator of the beginning of engagement or disengagement in an individual.

A machine-learning approach for stress detection using wearable sensors in free-living environments.

Stress is a psychological condition resulting from the body's response to challenging situations, which can negatively impact physical and mental health if experienced over prolonged periods. Early detection of stress is crucial to prevent chronic health problems. Wearable sensors offer an effective solution for continuous and real-time stress monitoring due to their non-intrusive nature and ability to monitor vital signs, e.g., heart rate and activity. Typically, most existing research has focused on data collected in controlled environments. Yet, our study aims to propose a machine learning-based approach for detecting stress in a free-living environment using wearable sensors. We utilized the SWEET dataset, which includes data from 240 subjects collected via electrocardiography (ECG), skin temperature (ST), and skin conductance (SC). We assessed four machine learning models, i.e., K-Nearest Neighbors (KNN), Support Vector Classification (SVC), Decision Tree (DT), Random Forest (RF), and XGBoost (XGB) in four different settings. This study evaluates the performance of various machine learning models for stress classification using the SWEET dataset. The analysis included two binary classification scenarios (with and without SMOTE) and two multi-class classification scenarios (with and without SMOTE). The Random Forest model demonstrated superior performance in the binary classification without SMOTE, achieving an accuracy of 98.29 % and an F1-score of 97.89 %. For binary classification with SMOTE, the K-Nearest Neighbors model performed best, with an accuracy of 95.70 % and an F1-score of 95.70 %. In the three-level classification without SMOTE, the Random Forest model again excelled, achieving an accuracy of 97.98 % and an F1-score of 97.22 %. For three-level classification with SMOTE, XGBoost showed the highest performance, with an accuracy and F1-score of 98.98 %. These results highlight the effectiveness of different models under various conditions, emphasizing the importance of model selection and preprocessing techniques in enhancing classification performance.

Autonomic Dysfunction in Amyotrophic Lateral Sclerosis - A Case-Control Study.

INTRODUCTION: This study aimed to explore autonomic nervous system involvement in amyotrophic lateral sclerosis (ALS) patients by evaluating sympathetic skin response (SSR). MATERIALS AND METHODS: The study included 35 sporadic (ALS) patients (cases), and 35 healthy age and sex-matched participants (controls) aged <60 years. SSR was recorded in the electrophysiology lab of the Neurology Department of Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, Bangladesh. Patients with diseases associated with peripheral or autonomic neuropathy were excluded. Prolonged latency (delayed SSR) or an absent response was considered abnormal SSR. RESULTS: SSR was found to be abnormal in 17 (48.6 %) ALS cases, with an absent response in the upper limbs of six cases (17.1%). Abnormal SSR was more prevalent in the lower limbs, with 33 (94.3%) and 20 (57.1%) cases having a delayed or absent response, respectively. In comparison, SSR was normal in all control participants (P-value <0.05). Abnormal SSR was significantly more common in the lower limbs of ALS cases with bulbar palsy than those without bulbar palsy (P-value=0.04). There was no association of SSR with disease severity and duration. CONCLUSION: ALS is significantly associated with abnormal SSR, indicating autonomic nervous system involvement. There could also be an association between bulbar palsy and abnormal SSR among ALS patients. Further studies should be carried out to determine the association of abnormal SSR with disease severity, duration, and type.

The effect of music on stress recovery.

OBJECTIVE: Previous research has suggested beneficial effects of music in reducing stress levels. However, there is no consistent conclusion demonstrating that music can contribute to stress recovery, primarily due to limitations in stress measurement, and inconsistent methodology within existing studies. Our study explores whether relaxing music, especially when self-selected, outperforms non-music acoustic, and silence conditions, fostering both subjective and biological stress recovery. METHODS: One hundred and five healthy female participants underwent the Trier Social Stress Test (TSST) before being randomly allocated to one of four conditions: condition 1 (n = 25) listened to researcher-selected relaxing music; condition 2 (n = 27) listened to self-selected relaxing music; condition 3 (n = 26) listened to the sound of rippling water; and condition 4 (n = 27) remained in silence. Stress parameters were repeatedly measured nine times before and after the TSST. Saliva samples were collected for cortisol and saliva alpha-amylase (sAA) analysis, Movisens equipment was used to measure heart rate (HR) and skin conductance levels (SCL), and Visual Analogue Scales (VAS) were used for subjective stress measurement. RESULTS: The examination of SCL, and VAS scores revealed no significant changes following the four relaxation interventions. Although sAA displayed a significant main effect of condition, post hoc tests did not pinpoint specific differences. HR recovery patterns varied among the four relaxation interventions, with the sound of rippling water condition exhibiting a later significant decrease compared to the other conditions. Exploratory analyses revealed that cortisol levels continued increase in all conditions during intervention phase except the researcher-selected music condition. CONCLUSIONS: The subjective and biological stress markers did not exhibit better recovery after the music stimulus, except for a tendency in the researcher-selected music condition to mitigate the continued increase in cortisol levels after the stress test. Our study provides the first evidence comparing the impact of researcher- and participant-selected music with silence and a non-music acoustic stimulus, on both subjective and biological stress recovery. Our findings contribute to a more nuanced understanding of the impact of music on stress recovery.