Electrodermal Activity Data Research Articles

Comparative analysis of electrodermal activity metrics and their association with child behavior in autism spectrum disorder.

Researchers are increasingly utilizing physiological data like electrodermal activity (EDA) to understand how stress "gets under the skin." Results of EDA studies in autistic children are mixed, with some suggesting autistic hyperarousal, others finding hypoarousal, and yet others detecting no difference compared to non-autistics. Some of this variability likely stems from the different techniques used to assess EDA. Therefore, the purpose of this study is to investigate and compare commonly used metrics of EDA (frequency of peaks, average amplitude of peaks, and standard deviation of skin conductance level) using two data processing programs (NeuroKit2 and Ledalab) and their link to observed child behavior. EDA data were collected using Empatica E4 wristbands from 60 autistic children and adolescents (5-18years old) during a 7-min play interaction with their primary caregiver. The play interaction was coded for a range of child behaviors including mood, social responsiveness, dysregulation, and cooperation. Results indicate a strong correlation between NeuroKit2 and Ledalab and a weak correlation between metrics within each program. Furthermore, the frequency of peaks was associated with more positive child social behaviors, and the magnitude of peaks was associated with less adaptive child behaviors. Recommendations for replication and the need for generalizability of this research are given.

Logistic Regression Based Model for Pain Intensity Level Detection from Biomedical Signal

Hospitals today make the effort to assess acute pain with self-report assessments such as the quantitative pain the level of intensity Index and visual input index. However, because these techniques rely on patient input, they are imprecise. Thus, an objective, statistical approach to ongoing pain monitoring is necessary. In computer vision research, identifying pain intensity is a difficult challenge to solve. However, current subjective pain evaluation is unreliable because it heavily relies on the patient's response. In order to improve the standardization of pain evaluation, automated pain identification using physiological data might provide essential objective information. In the present study, we provide an objective pain recognition approach based on physiological signals that can extract novel features from electromyography (EMG), electrodermal activity (EDA), and electrocardiogram (ECG) data that have not previously been utilized for pain recognition. Utilizing the Bio-Vid Heat Pain Database (Part A) for evaluation and clinical validation, the proposed machine learning logistic regression-based method performs significantly better than previous techniques recorded in the literature for both the electrodermal activity (EDA) and fusion approaches, with average performances of 82.36% and 83.20% for the binary classification experiment that discriminates within the baseline and the pain tolerance level (T0 vs. T4). Our result shows that it outperforms most of the previously proposed methods in related works.

Reading Between the Heat

Stress impacts our physical and mental health as well as our social life. A passive and contactless indoor stress monitoring system can unlock numerous important applications such as workplace productivity assessment, smart homes, and personalized mental health monitoring. While the thermal signatures from a user's body captured by a thermal camera can provide important information about the "fight-flight" response of the sympathetic and parasympathetic nervous system, relying solely on thermal imaging for training a stress prediction model often lead to overfitting and consequently a suboptimal performance. This paper addresses this challenge by introducing ThermaStrain, a novel co-teaching framework that achieves high-stress prediction performance by transferring knowledge from the wearable modality to the contactless thermal modality. During training, ThermaStrain incorporates a wearable electrodermal activity (EDA) sensor to generate stress-indicative representations from thermal videos, emulating stress-indicative representations from a wearable EDA sensor. During testing, only thermal sensing is used, and stress-indicative patterns from thermal data and emulated EDA representations are extracted to improve stress assessment. The study collected a comprehensive dataset with thermal video and EDA data under various stress conditions and distances. ThermaStrain achieves an F1 score of 0.8293 in binary stress classification, outperforming the thermal-only baseline approach by over 9%. Extensive evaluations highlight ThermaStrain's effectiveness in recognizing stress-indicative attributes, its adaptability across distances and stress scenarios, real-time executability on edge platforms, its applicability to multi-individual sensing, ability to function on limited visibility and unfamiliar conditions, and the advantages of its co-teaching approach. These evaluations validate ThermaStrain's fidelity and its potential for enhancing stress assessment.

Neurophysiological mental fatigue assessment for developing user-centered Artificial Intelligence as a solution for autonomous driving.

The human factor plays a key role in the automotive field since most accidents are due to drivers' unsafe and risky behaviors. The industry is now pursuing two main solutions to deal with this concern: in the short term, there is the development of systems monitoring drivers' psychophysical states, such as inattention and fatigue, and in the medium-long term, there is the development of fully autonomous driving. This second solution is promoted by recent technological progress in terms of Artificial Intelligence and sensing systems aimed at making vehicles more and more accurately aware of their "surroundings." However, even with an autonomous vehicle, the driver should be able to take control of the vehicle when needed, especially during the current transition from the lower (SAE < 3) to the highest level (SAE = 5) of autonomous driving. In this scenario, the vehicle has to be aware not only of its "surroundings" but also of the driver's psychophysical state, i.e., a user-centered Artificial Intelligence. The neurophysiological approach is one the most effective in detecting improper mental states. This is particularly true if considering that the more automatic the driving will be, the less available the vehicular data related to the driver's driving style. The present study aimed at employing a holistic approach, considering simultaneously several neurophysiological parameters, in particular, electroencephalographic, electrooculographic, photopletismographic, and electrodermal activity data to assess the driver's mental fatigue in real time and to detect the onset of fatigue increasing. This would ideally work as an information/trigger channel for the vehicle AI. In all, 26 professional drivers were engaged in a 45-min-lasting realistic driving task in simulated conditions, during which the previously listed biosignals were recorded. Behavioral (reaction times) and subjective measures were also collected to validate the experimental design and to support the neurophysiological results discussion. Results showed that the most sensitive and timely parameters were those related to brain activity. To a lesser extent, those related to ocular parameters were also sensitive to the onset of mental fatigue, but with a delayed effect. The other investigated parameters did not significantly change during the experimental session.

Simulation of ambulatory electrodermal activity and the handling of low-quality segments

Background and objectivesMonitoring electrodermal activity (EDA) in daily life requires effective handling of low-quality segments, which are common in ambulatory EDA data. Although several low-quality handling methods have been implemented, systematic comparison of these methods, which requires a large annotated dataset, is lacking. MethodsTherefore, we proposed the simulation of realistic ambulatory EDA data starting from high-quality EDA signals, which were subsequently contaminated with varying concentrations of artifacts. Subsequently, three approaches for handling low-quality data were evaluated regarding the preservation of several EDA-derived features: removing all artifacts, interpolating over removed artifacts, and retaining all artifacts. Specifically, multiple EDA features were assessed, derived from response detection (evaluated using F1, precision, recall) as well as EDA, phasic, and tonic features (assessed using absolute error), by comparing the simulated EDA data with and without the inserted artifacts, using the latter as ground truth. ResultsFor response detection, retaining artifacts resulted in the highest F1-scores, while interpolating over removed artifacts achieved the highest F1-scores for the phasic signal. The approaches did significantly differ in the mean error for the phasic but not for the tonic component and raw EDA. ConclusionThis work generated ambulatory EDA datasets of 200 h, containing 0.125 to 3 artifacts per minute, and showed that interpolation over removed artifacts was an effective approach to reconstruct phasic-derived features up to 2 artifacts per minute. The proposed simulation and evaluation methodology, which are easily customizable, offer opportunities for future research to develop and systematically compare signal quality indicators, decomposition methods, and response detectors for processing ambulatory EDA.

Denoising physiological data collected during multi-band, multi-echo EPI sequences

Collecting physiological data during functional magnetic resonance imaging (fMRI) experiments can improve fMRI data cleaning and contribute to our understanding of psychophysiological processes; however, these recordings are frequently fraught with artifacts from the MRI pulse sequence. Here, we assess data from BIOPAC Systems, Inc., one of the most widely used manufacturers of physiological monitoring equipment, evaluate their recommendations for filtering such artifacts from electrocardiogram and electrodermal activity data collected during single-band, single-echo fMRI sequences, and extend these recommendations to address artifacts associated with multi-band, multi-echo fMRI sequences. While the magnitude and frequencies of artifacts differ with these aspects of pulse sequences, their effects can be mitigated via application of digital filters incorporating slice collection, multi-band factor, and repetition time. The implementation of these filters is provided both in interactive online notebooks and an open-source denoising tool.

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.

Effects of mindfulness-based stress reduction meditation on the emotional reaction to affective pictures assessed by electrodermal activity

ObjectiveIn recent years the psychophysiological benefits of Mindfulness meditation on emotional processing have drawn great interest in scientific research. Currently, the effects of this meditation practice on stress, anxiety and well-being have been mostly evaluated using self-reporting questionnaires, which lead to a quite subjective assessment. This study assesses the effect of Mindfulness practice on the reaction to emotionally charged visual stimuli through Electrodermal Activity (EDA) data. MethodsTwenty-five healthy volunteers, without any previous experience of meditation techniques completed a 12-week Mindfulness-Based Stress Reduction (MBSR) course. EDA and psychological measures were collected longitudinally in 4 scheduled sessions. Statistical analysis was performed to find changes in the most relevant EDA parameters throughout the 4 sessions of data collection. ResultsWe found an increase in response latency, and a decrease in amplitude, area, number of specific responses, and skin conductance level along Mindfulness training. Both outcomes might suggest a reduction in the reactivity to the presented stimuli and an improvement in the emotional well-being of the practitioners. Furthermore, this study showed preliminary evidence that women improve more their attitude towards stressful stimuli than man, after the mindfulness practice.The statistical analysis also showed a correlation between the main EDA parameters and the scores reported by each participant in the depression, anxiety, and stress scale (DASS) questionnaire. Conclusion and SignificanceThis study contributed to a more objective evaluation of the physiological changes observed during Mindfulness practice, and so to understand the underlying mechanisms that explain the benefits of meditation training.

Listener- Versus Speaker-Oriented Disfluencies in Autistic Adults: Insights From Wearable Eye-Tracking and Skin Conductance Within a Live Face-to-Face Paradigm.

Our study addresses three main questions: (a) Do autistics and neurotypicals produce different patterns of disfluencies, depending on the experimenter's direct versus averted gaze? (b) Are these patterns correlated to gender, skin conductance responses, fixations on the experimenter's face, alexithymia, or social anxiety scores? Lastly, (c) can eye-tracking and electrodermal activity data be used in distinguishing listener- versus speaker-oriented disfluencies? Within a live face-to-face paradigm combining a wearable eye-tracker with electrodermal activity sensors, 80 adults (40 autistics, 40 neurotypicals) defined words in front of an experimenter who was either staring at their eyes (direct gaze condition) or looking elsewhere (averted gaze condition). Autistics produce less listener-oriented (uh, um) and more speaker-oriented (prolongations, breath) disfluencies than neurotypicals. In both groups, men produce less um than women. Both autistics' and neurotypicals' speech are influenced by whether their interlocutor systematically looks at them in the eyes or not, but their reactions go in opposite directions. Disfluencies seem to primarily be linguistic phenomena as experienced stress, social attention, alexithymia, and social anxiety scores do not influence any of the reported results. Finally, eye-tracking and electrodermal activity data suggest that laughter could be a listener-oriented disfluency. This article studies disfluencies in a fine-grained way in autistic and neurotypical adults while controlling for social attention, experienced stress, and experimental condition (direct vs. averted gaze). It adds to current literature by (a) enlightening our knowledge of speech in autism, (b) opening new perspectives on disfluency patterns as important signals in social interaction, (c) addressing theoretical issues on the dichotomy between listener- and speaker-oriented disfluencies, and (d) considering understudied phenomena as potential disfluencies (e.g., laughter, breath). https://doi.org/10.23641/asha.23549550.

"Where does it hurt?": Exploring EDA Signals to Detect and Localise Acute Pain.

Pain is a highly unpleasant sensory experience, for which currently no objective diagnostic test exists to measure it. Identification and localisation of pain, where the subject is unable to communicate, is a key step in enhancing therapeutic outcomes. Numerous studies have been conducted to categorise pain, but no reliable conclusion has been achieved. This is the first study that aims to show a strict relation between Electrodermal Activity (EDA) signal features and the presence of pain and to clarify the relation of classified signals to the location of the pain. For that purpose, EDA signals were recorded from 28 healthy subjects by inducing electrical pain at two anatomical locations (hand and forearm) of each subject. The EDA data were preprocessed with a Discrete Wavelet Transform to remove any irrelevant information. Chi-square feature selection was used to select features extracted from three domains: time, frequency, and cepstrum. The final feature vector was fed to a pool of classification schemes where an Artificial Neural Network classifier performed best. The proposed method, evaluated through leave-one-subject-out cross-validation, provided 90% accuracy in pain detection (no pain vs. pain), whereas the pain localisation experiment (hand pain vs. forearm pain) achieved 66.67% accuracy.Clinical relevance- This is the first study to provide an analysis of EDA signals in finding the source of the pain. This research explores the viability of using EDA for pain localisation, which may be helpful in the treatment of noncommunicable patients.

Wearable Technologies for Electrodermal and Cardiac Activity Measurements: A Comparison between Fitbit Sense, Empatica E4 and Shimmer GSR3.

The capability of measuring specific neurophysiological and autonomic parameters plays a crucial role in the objective evaluation of a human's mental and emotional states. These human aspects are commonly known in the scientific literature to be involved in a wide range of processes, such as stress and arousal. These aspects represent a relevant factor especially in real and operational environments. Neurophysiological autonomic parameters, such as Electrodermal Activity (EDA) and Photoplethysmographic data (PPG), have been usually investigated through research-graded devices, therefore resulting in a high degree of invasiveness, which could negatively interfere with the monitored user's activity. For such a reason, in the last decade, recent consumer-grade wearable devices, usually designed for fitness-tracking purposes, are receiving increasing attention from the scientific community, and are characterized by a higher comfort, ease of use and, therefore, by a higher compatibility with daily-life environments. The present preliminary study was aimed at assessing the reliability of a consumer wearable device, i.e., the Fitbit Sense, with respect to a research-graded wearable, i.e., the Empatica E4 wristband, and a laboratory device, i.e., the Shimmer GSR3+. EDA and PPG data were collected among 12 participants while they performed multiple resting conditions. The results demonstrated that the EDA- and PPG-derived features computed through the wearable and research devices were positively and significantly correlated, while the reliability of the consumer device was significantly lower.

Measuring Physiological Responses to Visualizations of Urban Planning Scenarios in Immersive Virtual Reality

Stakeholder participation is an important component of modern urban planning processes. It can provide information about potential social conflicts related to specific urban planning scenarios. However, acquiring feedback from stakeholders is usually limited to explicit response types such as interviews or questionnaires. Such explicit response types are not suitable for the assessment of unconscious responses to specific parameters of an urban planning scenario. To address this limitation, we propose an approach for the assessment of affective and stress responses using implicit measures. Using a measure for electrodermal activity (EDA) and a virtual reality (VR)-based 3D urban model, we demonstrate how implicit physiological measurements can be visualized and temporally matched to specific parameters in an immersive representation of an urban planning scenario. Since this approach is supposed to support conventional stakeholder participation processes in urban planning, we designed it to be simple, cost-effective and with as little task interference as possible. Based on the additional insights gained from measuring physiological responses to urban planning scenarios, urban planners can further optimize planning scenarios by adjusting them to the derived implicitly expressed needs of stakeholders. To support simple implementation of the suggested approach, we provide sample scripts for visualization of EDA data. Limitations concerning the evaluation of raw EDA data and potentials for extending the described approach with additional physiological measures and real-time data evaluation are discussed.

Stressed or engaged? Addressing the mixed significance of physiological activity during constructivist learning

The current paper qualifies previous assumptions on the link between physiology and learner states during constructivist learning, employing a study design where electrodermal activity data (EDA) is collected across two distinctive pedagogies -- direct instruction and hands-on learning. Our results are surprising in that higher arousal during hands-on learning is not indicative of higher learning gains, nor is the strength of the EDA response clearly different when transitioning across pedagogies. However, qualitative investigations show that varying sources of physiological arousal likely to be present in hands-on learning, such as stress, may have led to this mixed connection between EDA and learning during hands-on learning. Moreover, identical tests for the direct instruction section show that higher EDA during this more passive form of learning is indeed indicative of higher learning gains. Our machine learning prediction models achieved fair performance for both learning gains and engagement, providing support for the use of physiological data in gauging and supporting learner states despite its polysemy. We conclude with informed suggestions for building theory-based, nuanced connections between EDA and constructivist learning.

A Computational Approach to Examining Team Coordination Breakdowns During Crisis Situations

During crisis situations, teams are more prone to coordination breakdowns that are characterized by a temporary, diminished ability to function effectively as a team. However, team research currently lacks robust approaches for identifying transitions from effective team functioning to coordination breakdowns. With the current study, we aimed to develop such robust approaches, and to deepen our understanding of how team coordination dynamics across various physiological signals reflect coordination breakdowns. Consequently, we used audiovisual data from four-person teams involved in a stressful collaborative game task to manually identify coordination breakdowns. Next, we set out to computationally identify coordination breakdowns by applying continuous measures of team coordination (windowed synchronization coefficient and multidimensional recurrence quantification analysis) to photoplethysmogram and electrodermal activity data obtained during the task, and identifying transitions therein with change point and nonlinear prediction algorithms. We found that our computational coordination breakdown identification approaches can identify up to 96% of the manually identified coordination breakdowns although our results also show that the precision of our approaches falls far behind. Our findings contribute theoretically and methodologically to the systematic investigation of coordination breakdowns, which may ultimately facilitate the support of teams in responding to and mitigating negative consequences of crisis situations.

Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation Entropy.

As technology becomes more sophisticated, more accessible methods of interpretating Big Data become essential. We have continued to develop Complexity and Entropy in Physiological Signals (CEPS) as an open access MATLAB® GUI (graphical user interface) providing multiple methods for the modification and analysis of physiological data. To demonstrate the functionality of the software, data were collected from 44 healthy adults for a study investigating the effects on vagal tone of breathing paced at five different rates, as well as self-paced and un-paced. Five-minute 15-s recordings were used. Results were also compared with those from shorter segments of the data. Electrocardiogram (ECG), electrodermal activity (EDA) and Respiration (RSP) data were recorded. Particular attention was paid to COVID risk mitigation, and to parameter tuning for the CEPS measures. For comparison, data were processed using Kubios HRV, RR-APET and DynamicalSystems.jl software. We also compared findings for ECG RR interval (RRi) data resampled at 4 Hz (4R) or 10 Hz (10R), and non-resampled (noR). In total, we used around 190-220 measures from CEPS at various scales, depending on the analysis undertaken, with our investigation focused on three families of measures: 22 fractal dimension (FD) measures, 40 heart rate asymmetries or measures derived from Poincaré plots (HRA), and 8 measures based on permutation entropy (PE). FDs for the RRi data differentiated strongly between breathing rates, whether data were resampled or not, increasing between 5 and 7 breaths per minute (BrPM). Largest effect sizes for RRi (4R and noR) differentiation between breathing rates were found for the PE-based measures. Measures that both differentiated well between breathing rates and were consistent across different RRi data lengths (1-5 min) included five PE-based (noR) and three FDs (4R). Of the top 12 measures with short-data values consistently within ± 5% of their values for the 5-min data, five were FDs, one was PE-based, and none were HRAs. Effect sizes were usually greater for CEPS measures than for those implemented in DynamicalSystems.jl. The updated CEPS software enables visualisation and analysis of multichannel physiological data using a variety of established and recently introduced complexity entropy measures. Although equal resampling is theoretically important for FD estimation, it appears that FD measures may also be usefully applied to non-resampled data.

Feasibility of Electrodermal Activity and Photoplethysmography Data Acquisition at the Foot Using a Sock Form Factor.

Wearable devices have been shown to play an important role in disease prevention and health management, through the multimodal acquisition of peripheral biosignals. However, many of these wearables are exposed, limiting their long-term acceptability by some user groups. To overcome this, a wearable smart sock integrating a PPG sensor and an EDA sensor with textile electrodes was developed. Using the smart sock, EDA and PPG measurements at the foot/ankle were performed in test populations of 19 and 15 subjects, respectively. Both measurements were validated by simultaneously recording the same signals with a standard device at the hand. For the EDA measurements, Pearson correlations of up to 0.95 were obtained for the SCL component, and a mean consensus of 69% for peaks detected in the two locations was obtained. As for the PPG measurements, after fine-tuning the automatic detection of systolic peaks, the index finger and ankle, accuracies of 99.46% and 87.85% were obtained, respectively. Moreover, an HR estimation error of 17.40±14.80 Beats-Per-Minute (BPM) was obtained. Overall, the results support the feasibility of this wearable form factor for unobtrusive EDA and PPG monitoring.

Interfaces of Dialogue: A Mixed Methods Approach to Investigating Intersubjectivity in Dyadic Improvisations

Music therapy provides a complex and dynamic environment and as such offers space for the emergence of nonverbal dialogue. We aimed to capture the relational, musical, and physiological aspects of intersubjectivity emerging during dyadic piano improvisations between music therapists and adult participants in a non-clinical feasibility study. Various data from 17 partner improvisations with adult participants and music therapists were collected, including audio, video, and MIDI recordings, physiological (ECG and EDA) measures, questionnaires, and semi-structured debriefing interviews. Parts of these data sets served for a mixed methods analysis to show (1) how instances of intersubjectivity were experienced by therapists and participants (drawing on interview data), (2) how their shared experiences were supported by coordination and contrast at the level of musical features (drawing on musical data), (3) how physiological arousal was compared between an initial resting baseline and the improvisation, and (4) how patterns in the event related electrodermal activity data related to qualities of intersubjectivity and musical interaction in an example of two case studies. In conclusion, intersubjectivity was characterized by overlapping experiences of relational qualities and complex patterns of musical interaction, including contrasting elements of convergence (in pitch contour) and divergence (key velocity and timing). Further, the case studies showed how differently-interpreted experiences can emerge from a homogenous musical structure, and how stronger intersubjectivity may be associated with more active experiential phenomena such as disruption or reciprocity (versus relaxation or security). Additionally, our findings highlight the complexity of musical interaction as an enactive process. Bringing together multi- and interdisciplinary perspectives enhanced a shared understanding of how subjective experiences that arise during dyadic improvisations relate to measures of musical coordination and physiological arousal. The complex mixed methods design proves to be feasible and, as a consequence, can be transferred to clinical settings in the future.

Incorporating the Effect of Behavioral States in Multi-Step Ahead Deep Learning Based Multivariate Predictors for Blood Glucose Forecasting in Type 1 Diabetes

Behavioral factors can affect the blood glucose (BG) levels in people with type 1 diabetes (T1D), therefore, their effects need to be incorporated in blood glucose management for these individuals. Accordingly, in this work, we study the effect of two behavioral states, physical activity (PA) and stress state (SS), on BG fluctuations in individuals with T1D. We provide two methods for quantifying biomarkers related to PA and SS using raw acceleration (ACC) and electrodermal activity (EDA) data collected with a wearable device. We evaluate the impact of PA and SS on BG fluctuation by adding the derived behavior-related biomarkers in two cutting-edge deep learning-based glucose predictive models, a long short-term memory (LSTM) and a convolutional neural network (CNN)-LSTM network, for prediction horizons (PHs) of 30, 60 and 90 min. Through an ablation study, we demonstrate that incorporating the estimated behavior-related biomarkers improves the BG predictive model’s performance obtaining mean absolute error (MAE) 9.13 ± 0.95, 17.75 ± 1.93 and 31.85 ± 2.88 in [mg/dL], root mean square error (RMSE), 12.35 ± 1.06, 24.71 ± 2.31 and 41.64 ± 4.12 in [mg/dL], and coefficient of determination (R2), 95.34 ± 3.34, 78.87 ± 4.35 and 60.11 ± 4.76 in [%], for the LSTM model; and MAE 9.37 ± 0.88, 17.87 ± 1.67 and 29.47 ± 2.13 in [mg/dL], RMSE 12.51 ± 1.40, 24.37 ± 2.49 and 39.52 ± 3.89 in [mg/dL], and R2 94.65 ± 3.90, 78.37 ± 4.11 and 61.12 ± 4.30 in [%], for the CNN-LSTM model, respectively, across all PHs. Additionally, we illustrate the generalizability of the proposed models by performing both population- and patient-wise.

Toward Affirmation of Recovery of Deeply Embedded Autobiographical Memory with Background Music and Identification of an EEG Biomarker in Combination with EDA Signal Using Wearable Sensors

There is no disputing the role that background music plays in memory recall. Music has the power to activate the brain and trigger deeply ingrained memories. For dementia patients, background music is a common therapy because of this. Previous studies used music to recall lyrics, series of words, and long- and short-term memories. In this research, electroencephalogram (EEG) and electrodermal activity (EDA) data are collected from 40 healthy participants using wearable sensors during nine music sessions (three happy, three sad, and three neutral). A post-study survey is given to all participants after each piece of music to know if they recalled any autobiographical memories. The main objective is to find an EEG biomarker using the collected qualitative and quantitative data for autobiographical memory recall. The study finds that for all four EEG channels, alpha power rises considerably (on average 16.2%) during the memory “recall” scenario (F3: p = 0.0066, F7: p = 0.0386, F4: p = 0.0023, and F8: p = 0.0288) compared to the “no-recall” situation. Beta power also increased significantly for two channels (F3: p = 0.0100 and F4: p = 0.0210) but not for others (F7: p = 0.6792 and F8: p = 0.0814). Additionally, the phasic standard deviation (p = 0.0260), phasic max (p = 0.0011), phasic energy (p = 0.0478), tonic min (p = 0.0092), tonic standard deviation (p = 0.0171), and phasic energy (p = 0.0478) are significantly different for the EDA signal. The authors conclude by interpreting increased alpha power (8–12 Hz) as a biomarker for autobiographical memory recall.

A Deep Convolutional Autoencoder for Automatic Motion Artifact Removal in Electrodermal Activity.

This study aimed to develop a robust and data driven automatic motion artifacts (MA) removal technique from electrodermal activity (EDA) signal. we proposed a deep convolutional autoencoder (DCAE) approach for automatic MA removal in EDA signals. Our model was trained using several publicly available datasets that were collected using a wide variety of stimuli to cause EDA reactions; the sample size was large ([Formula: see text]). We trained and validated our DCAE network using both Gaussian white noise (GWN) and realistic MA data records collected using a novel circuitry in our lab. We further evaluated and compared the performance of our DCAE model with the existing methods on two independent and unseen datasets called Chon lab motion artifact dataset II (CMAD II) and central nervous system oxygen toxicity dataset (CNS-OT). Our DCAE model showed significantly higher signal-to-noise-power-ratio improvement ( SNRimp) and lower mean squared error ( MSE) when compared with that of the three previous methods (averaged [Formula: see text], and MSE = 0.028 on the MA-corrupted data). Moreover, the reconstructed EDAs from the CMAD II dataset had a mean correlation value of 0.78 (statistically significantly higher when compared with other methods) with the reference clean data from the motionless hand, whereas the raw MA-corrupted data had a correlation value of only 0.68. The results presented in the paper indicates that our DCAE can remove MAs with higher intensity where the existing methods fails. Proposed DCAE model can be used to recover a significant amount of otherwise discarded EDA data.