The Experts below are selected from a list of 6063 Experts worldwide ranked by ideXlab platform
Rosalind W. Picard - One of the best experts on this subject based on the ideXlab platform.
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multiple arousal theory and daily life Electrodermal Activity asymmetry
Emotion Review, 2016Co-Authors: Rosalind W. Picard, Szymon Fedor, Yadid AyzenbergAbstract:Using “big data” from sensors worn continuously outside the lab, researchers have observed patterns of objective physiology that challenge some of the long-standing theoretical concepts of emotion and its measurement. One challenge is that emotional arousal, when measured as sympathetic nervous system activation through Electrodermal Activity, can sometimes differ significantly across the two halves of the upper body. We show that traditional measures on only one side may lead to misjudgment of arousal. This article presents daily life and controlled study data, as well as existing evidence from neuroscience, supporting the influence of multiple emotional substrates in the brain causing innervation on different sides of the body. We describe how a theory of multiple arousals explains the asymmetric EDA findings.
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automatic identification of artifacts in Electrodermal Activity data
International Conference of the IEEE Engineering in Medicine and Biology Society, 2015Co-Authors: Sara Taylor, Szymon Fedor, Natasha Jaques, Weixuan Chen, Akane Sano, Rosalind W. PicardAbstract:Recently, wearable devices have allowed for long term, ambulatory measurement of Electrodermal Activity (EDA). Despite the fact that ambulatory recording can be noisy, and recording artifacts can easily be mistaken for a physiological response during analysis, to date there is no automatic method for detecting artifacts. This paper describes the development of a machine learning algorithm for automatically detecting EDA artifacts, and provides an empirical evaluation of classification performance. We have encoded our results into a freely available web-based tool for artifact and peak detection.
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wavelet based motion artifact removal for Electrodermal Activity
International Conference of the IEEE Engineering in Medicine and Biology Society, 2015Co-Authors: Weixuan Chen, Szymon Fedor, Sara Taylor, Natasha Jaques, Akane Sano, Rosalind W. PicardAbstract:Electrodermal Activity (EDA) recording is a powerful, widely used tool for monitoring psychological or physiological arousal. However, analysis of EDA is hampered by its sensitivity to motion artifacts. We propose a method for removing motion artifacts from EDA, measured as skin conductance (SC), using a stationary wavelet transform (SWT). We modeled the wavelet coefficients as a Gaussian mixture distribution corresponding to the underlying skin conductance level (SCL) and skin conductance responses (SCRs). The goodness-of-fit of the model was validated on ambulatory SC data. We evaluated the proposed method in comparison with three previous approaches. Our method achieved a greater reduction of artifacts while retaining motion-artifact-free data.
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quantitative analysis of wrist Electrodermal Activity during sleep
PMC, 2014Co-Authors: Akane Sano, Rosalind W. Picard, Robert StickgoldAbstract:We present the first quantitative characterization of Electrodermal Activity (EDA) patterns on the wrists of healthy adults during sleep using dry electrodes. We compare the new results on the wrist to the prior findings on palmar or finger EDA by characterizing data measured from 80 nights of sleep consisting of 9 nights of wrist and palm EDA from 9 healthy adults sleeping at home, 56 nights of wrist and palm EDA from one healthy adult sleeping at home, and 15 nights of wrist EDA from 15 healthy adults in a sleep laboratory, with the latter compared to concurrent polysomnography. While high frequency patterns of EDA called "storms" were identified by eye in the 1960s, we systematically compare thresholds for automatically detecting EDA peaks and establish criteria for EDA storms. We found that more than 80% of the EDA peaks occurred in non-REM sleep, specifically during slow-wave sleep (SWS) and non-REM stage 2 sleep (NREM2). Also, EDA amplitude is higher in SWS than in other sleep stages. Longer EDA storms were more likely to occur in the first two quarters of sleep and during SWS and NREM2. We also found from the home studies (65 nights) that EDA levels were higher and the skin conductance peaks were larger and more frequent when measured on the wrist than when measured on the palm. These EDA high frequency peaks and high amplitude were sometimes associated with higher skin temperature, but more work is needed looking at neurological and other EDA elicitors in order to elucidate their complete behavior.
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using Electrodermal Activity to recognize ease of engagement in children during social interactions
Ubiquitous Computing, 2014Co-Authors: Javier Hernandez, Ivan Riobo, Agata Rozga, Gregory D Abowd, Rosalind W. PicardAbstract:The recent emergence of comfortable wearable sensors has focused almost entirely on monitoring physical Activity, ignoring opportunities to monitor more subtle phenomena, such as the quality of social interactions. We argue that it is compelling to address whether physiological sensors can shed light on quality of social interactive behavior. This work leverages the use of a wearable Electrodermal Activity (EDA) sensor to recognize ease of engagement of children during a social interaction with an adult. In particular, we monitored 51 child-adult dyads in a semi-structured play interaction and used Support Vector Machines to automatically identify children who had been rated by the adult as more or less difficult to engage. We report on the classification value of several features extracted from the child's EDA responses, as well as several other features capturing the physiological synchrony between the child and the adult.
Enzo Pasquale Scilingo - One of the best experts on this subject based on the ideXlab platform.
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acute stress state classification based on Electrodermal Activity modeling
IEEE Transactions on Affective Computing, 2021Co-Authors: Alberto Greco, Gaetano Valenza, Jesus Lazaro, Jorge M Garzonrey, Jordi Aguilo, Concepcion Delacamara, Raquel Bailon, Enzo Pasquale ScilingoAbstract:Acute stress is a physiological condition that may induce several neural dysfunctions with a significant impact on life quality. Accordingly, it would be important to monitor stress in everyday life unobtrusively and inexpensively. In this paper, we presented a new methodological pipeline to recognize acute stress conditions using Electrodermal Activity (EDA) exclusively. Particularly, we combined a rigorous and robust model (cvxEDA) for EDA processing and decomposition, with an algorithm based on a support vector machine to classify the stress state at a single-subject level. Indeed, our method, based on a single sensor, is robust to noise, applies a rigorous phasic decomposition, and implements an unbiased multiclass classification. To this end, we analyzed the EDA of 65 volunteers subjected to different acute stress stimuli induced by a modified version of the Trier Social Stress Test. Our results show that stress is successfully detected with an average accuracy of 94.62%. Besides, we proposed a further 4-class pattern recognition system able to distinguish between non-stress condition and three different stressful stimuli achieving an average accuracy as high as 75.00%. These results, obtained under controlled conditions, are the first step towards applications in ecological scenarios.
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force velocity assessment of caress like stimuli through the Electrodermal Activity processing advantages of a convex optimization approach
IEEE Transactions on Human-Machine Systems, 2017Co-Authors: Alberto Greco, Gaetano Valenza, Luca Citi, Mimma Nardelli, Matteo Bianchi, Enzo Pasquale ScilingoAbstract:We propose the use of the convex optimization-based EDA (cvxEDA) framework to automatically characterize the force and velocity of caressing stimuli through the analysis of the Electrodermal Activity (EDA). CvxEDA, in fact, solves a convex optimization problem that always guarantees the globally optimal solution. We show that this approach is especially suitable for the implementation in wearable monitoring systems, being more computationally efficient than a widely used EDA processing algorithm. In addition, it ensures low-memory consumption, due to a sparse representation of the EDA phasic components. EDA recordings were gathered from 32 healthy subjects (16 females) who participated in an experiment where a fabric-based wearable haptic system conveyed them caress-like stimuli by means of two motors. Six types of stimuli (combining three levels of velocity and two of force) were randomly administered over time. Performance was evaluated in terms of execution time of the algorithm, memory usage, and statistical significance in discerning the affective stimuli along force and velocity dimensions. Experimental results revealed good performance of cvxEDA model for all of the considered metrics.
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Arousal and Valence Recognition of Affective Sounds Based on Electrodermal Activity
IEEE Sensors Journal, 2017Co-Authors: Alberto Greco, Gaetano Valenza, Luca Citi, Enzo Pasquale ScilingoAbstract: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.
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advances in Electrodermal Activity processing with applications for mental health from heuristic methods to convex optimization
2016Co-Authors: Alberto Greco, Gaetano Valenza, Enzo Pasquale ScilingoAbstract:This book explores Autonomic Nervous System (ANS) dynamics as investigated through Electrodermal Activity (EDA) processing. It presents groundbreaking research in thetechnical field of biomedical engineering, especially biomedical signal processing, as well as clinical fields ofpsychometrics, affective computing, and psychological assessment. This volumedescribes some of the most complete, effective, and personalized methodologies for extracting data from a non-stationary, nonlinear EDA signal in order to characterize the affective and emotional state of a human subject. These methodologies are underscored by discussion of real-world applications in mood assessment. The text also examines the physiological bases of emotion recognition through noninvasivemonitoring of the autonomic nervous system. This is an ideal book forbiomedical engineers, physiologists,neuroscientists, engineers, applied mathmeticians, psychiatric and psychological clinicians, and graduate students in these fields. This book also:Expertly introduces a novel approach for EDA analysis based on convex optimization and sparsity, a topic of rapidly increasing interestAuthoritatively presents groundbreaking research achieved using EDA as an exemplarybiomarker of ANS dynamicsDeftly exploresEDA's potential as a source of reliable and effective markers for the assessment of emotional responses in healthy subjects,as well as for the recognition of pathological mood states in bipolar patients
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cvxEDA: A Convex Optimization Approach to Electrodermal Activity Processing
IEEE Transactions on Biomedical Engineering, 2016Co-Authors: Alberto Greco, Gaetano Valenza, Enzo Pasquale Scilingo, Antonio Lanata, Luca CitiAbstract:Goal: This paper reports on a novel algorithm for the analysis of Electrodermal Activity (EDA) using methods of convex optimization. EDA can be considered as one of the most common observation channels of sympathetic nervous system Activity, and manifests itself as a change in electrical properties of the skin, such as skin conductance (SC). Methods: The proposed model describes SC as the sum of three terms: the phasic component, the tonic component, and an additive white Gaussian noise term incorporating model prediction errors as well as measurement errors and artifacts. This model is physiologically inspired and fully explains EDA through a rigorous methodology based on Bayesian statistics, mathematical convex optimization, and sparsity. Results: The algorithm was evaluated in three different experimental sessions to test its robustness to noise, its ability to separate and identify stimulus inputs, and its capability of properly describing the Activity of the autonomic nervous system in response to strong affective stimulation. Significance: Results are very encouraging, showing good performance of the proposed method and suggesting promising future applicability, e.g., in the field of affective computing.
Alberto Greco - One of the best experts on this subject based on the ideXlab platform.
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acute stress state classification based on Electrodermal Activity modeling
IEEE Transactions on Affective Computing, 2021Co-Authors: Alberto Greco, Gaetano Valenza, Jesus Lazaro, Jorge M Garzonrey, Jordi Aguilo, Concepcion Delacamara, Raquel Bailon, Enzo Pasquale ScilingoAbstract:Acute stress is a physiological condition that may induce several neural dysfunctions with a significant impact on life quality. Accordingly, it would be important to monitor stress in everyday life unobtrusively and inexpensively. In this paper, we presented a new methodological pipeline to recognize acute stress conditions using Electrodermal Activity (EDA) exclusively. Particularly, we combined a rigorous and robust model (cvxEDA) for EDA processing and decomposition, with an algorithm based on a support vector machine to classify the stress state at a single-subject level. Indeed, our method, based on a single sensor, is robust to noise, applies a rigorous phasic decomposition, and implements an unbiased multiclass classification. To this end, we analyzed the EDA of 65 volunteers subjected to different acute stress stimuli induced by a modified version of the Trier Social Stress Test. Our results show that stress is successfully detected with an average accuracy of 94.62%. Besides, we proposed a further 4-class pattern recognition system able to distinguish between non-stress condition and three different stressful stimuli achieving an average accuracy as high as 75.00%. These results, obtained under controlled conditions, are the first step towards applications in ecological scenarios.
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force velocity assessment of caress like stimuli through the Electrodermal Activity processing advantages of a convex optimization approach
IEEE Transactions on Human-Machine Systems, 2017Co-Authors: Alberto Greco, Gaetano Valenza, Luca Citi, Mimma Nardelli, Matteo Bianchi, Enzo Pasquale ScilingoAbstract:We propose the use of the convex optimization-based EDA (cvxEDA) framework to automatically characterize the force and velocity of caressing stimuli through the analysis of the Electrodermal Activity (EDA). CvxEDA, in fact, solves a convex optimization problem that always guarantees the globally optimal solution. We show that this approach is especially suitable for the implementation in wearable monitoring systems, being more computationally efficient than a widely used EDA processing algorithm. In addition, it ensures low-memory consumption, due to a sparse representation of the EDA phasic components. EDA recordings were gathered from 32 healthy subjects (16 females) who participated in an experiment where a fabric-based wearable haptic system conveyed them caress-like stimuli by means of two motors. Six types of stimuli (combining three levels of velocity and two of force) were randomly administered over time. Performance was evaluated in terms of execution time of the algorithm, memory usage, and statistical significance in discerning the affective stimuli along force and velocity dimensions. Experimental results revealed good performance of cvxEDA model for all of the considered metrics.
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Arousal and Valence Recognition of Affective Sounds Based on Electrodermal Activity
IEEE Sensors Journal, 2017Co-Authors: Alberto Greco, Gaetano Valenza, Luca Citi, Enzo Pasquale ScilingoAbstract: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.
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advances in Electrodermal Activity processing with applications for mental health from heuristic methods to convex optimization
2016Co-Authors: Alberto Greco, Gaetano Valenza, Enzo Pasquale ScilingoAbstract:This book explores Autonomic Nervous System (ANS) dynamics as investigated through Electrodermal Activity (EDA) processing. It presents groundbreaking research in thetechnical field of biomedical engineering, especially biomedical signal processing, as well as clinical fields ofpsychometrics, affective computing, and psychological assessment. This volumedescribes some of the most complete, effective, and personalized methodologies for extracting data from a non-stationary, nonlinear EDA signal in order to characterize the affective and emotional state of a human subject. These methodologies are underscored by discussion of real-world applications in mood assessment. The text also examines the physiological bases of emotion recognition through noninvasivemonitoring of the autonomic nervous system. This is an ideal book forbiomedical engineers, physiologists,neuroscientists, engineers, applied mathmeticians, psychiatric and psychological clinicians, and graduate students in these fields. This book also:Expertly introduces a novel approach for EDA analysis based on convex optimization and sparsity, a topic of rapidly increasing interestAuthoritatively presents groundbreaking research achieved using EDA as an exemplarybiomarker of ANS dynamicsDeftly exploresEDA's potential as a source of reliable and effective markers for the assessment of emotional responses in healthy subjects,as well as for the recognition of pathological mood states in bipolar patients
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cvxEDA: A Convex Optimization Approach to Electrodermal Activity Processing
IEEE Transactions on Biomedical Engineering, 2016Co-Authors: Alberto Greco, Gaetano Valenza, Enzo Pasquale Scilingo, Antonio Lanata, Luca CitiAbstract:Goal: This paper reports on a novel algorithm for the analysis of Electrodermal Activity (EDA) using methods of convex optimization. EDA can be considered as one of the most common observation channels of sympathetic nervous system Activity, and manifests itself as a change in electrical properties of the skin, such as skin conductance (SC). Methods: The proposed model describes SC as the sum of three terms: the phasic component, the tonic component, and an additive white Gaussian noise term incorporating model prediction errors as well as measurement errors and artifacts. This model is physiologically inspired and fully explains EDA through a rigorous methodology based on Bayesian statistics, mathematical convex optimization, and sparsity. Results: The algorithm was evaluated in three different experimental sessions to test its robustness to noise, its ability to separate and identify stimulus inputs, and its capability of properly describing the Activity of the autonomic nervous system in response to strong affective stimulation. Significance: Results are very encouraging, showing good performance of the proposed method and suggesting promising future applicability, e.g., in the field of affective computing.
Gaetano Valenza - One of the best experts on this subject based on the ideXlab platform.
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acute stress state classification based on Electrodermal Activity modeling
IEEE Transactions on Affective Computing, 2021Co-Authors: Alberto Greco, Gaetano Valenza, Jesus Lazaro, Jorge M Garzonrey, Jordi Aguilo, Concepcion Delacamara, Raquel Bailon, Enzo Pasquale ScilingoAbstract:Acute stress is a physiological condition that may induce several neural dysfunctions with a significant impact on life quality. Accordingly, it would be important to monitor stress in everyday life unobtrusively and inexpensively. In this paper, we presented a new methodological pipeline to recognize acute stress conditions using Electrodermal Activity (EDA) exclusively. Particularly, we combined a rigorous and robust model (cvxEDA) for EDA processing and decomposition, with an algorithm based on a support vector machine to classify the stress state at a single-subject level. Indeed, our method, based on a single sensor, is robust to noise, applies a rigorous phasic decomposition, and implements an unbiased multiclass classification. To this end, we analyzed the EDA of 65 volunteers subjected to different acute stress stimuli induced by a modified version of the Trier Social Stress Test. Our results show that stress is successfully detected with an average accuracy of 94.62%. Besides, we proposed a further 4-class pattern recognition system able to distinguish between non-stress condition and three different stressful stimuli achieving an average accuracy as high as 75.00%. These results, obtained under controlled conditions, are the first step towards applications in ecological scenarios.
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force velocity assessment of caress like stimuli through the Electrodermal Activity processing advantages of a convex optimization approach
IEEE Transactions on Human-Machine Systems, 2017Co-Authors: Alberto Greco, Gaetano Valenza, Luca Citi, Mimma Nardelli, Matteo Bianchi, Enzo Pasquale ScilingoAbstract:We propose the use of the convex optimization-based EDA (cvxEDA) framework to automatically characterize the force and velocity of caressing stimuli through the analysis of the Electrodermal Activity (EDA). CvxEDA, in fact, solves a convex optimization problem that always guarantees the globally optimal solution. We show that this approach is especially suitable for the implementation in wearable monitoring systems, being more computationally efficient than a widely used EDA processing algorithm. In addition, it ensures low-memory consumption, due to a sparse representation of the EDA phasic components. EDA recordings were gathered from 32 healthy subjects (16 females) who participated in an experiment where a fabric-based wearable haptic system conveyed them caress-like stimuli by means of two motors. Six types of stimuli (combining three levels of velocity and two of force) were randomly administered over time. Performance was evaluated in terms of execution time of the algorithm, memory usage, and statistical significance in discerning the affective stimuli along force and velocity dimensions. Experimental results revealed good performance of cvxEDA model for all of the considered metrics.
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Arousal and Valence Recognition of Affective Sounds Based on Electrodermal Activity
IEEE Sensors Journal, 2017Co-Authors: Alberto Greco, Gaetano Valenza, Luca Citi, Enzo Pasquale ScilingoAbstract: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.
-
advances in Electrodermal Activity processing with applications for mental health from heuristic methods to convex optimization
2016Co-Authors: Alberto Greco, Gaetano Valenza, Enzo Pasquale ScilingoAbstract:This book explores Autonomic Nervous System (ANS) dynamics as investigated through Electrodermal Activity (EDA) processing. It presents groundbreaking research in thetechnical field of biomedical engineering, especially biomedical signal processing, as well as clinical fields ofpsychometrics, affective computing, and psychological assessment. This volumedescribes some of the most complete, effective, and personalized methodologies for extracting data from a non-stationary, nonlinear EDA signal in order to characterize the affective and emotional state of a human subject. These methodologies are underscored by discussion of real-world applications in mood assessment. The text also examines the physiological bases of emotion recognition through noninvasivemonitoring of the autonomic nervous system. This is an ideal book forbiomedical engineers, physiologists,neuroscientists, engineers, applied mathmeticians, psychiatric and psychological clinicians, and graduate students in these fields. This book also:Expertly introduces a novel approach for EDA analysis based on convex optimization and sparsity, a topic of rapidly increasing interestAuthoritatively presents groundbreaking research achieved using EDA as an exemplarybiomarker of ANS dynamicsDeftly exploresEDA's potential as a source of reliable and effective markers for the assessment of emotional responses in healthy subjects,as well as for the recognition of pathological mood states in bipolar patients
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cvxEDA: A Convex Optimization Approach to Electrodermal Activity Processing
IEEE Transactions on Biomedical Engineering, 2016Co-Authors: Alberto Greco, Gaetano Valenza, Enzo Pasquale Scilingo, Antonio Lanata, Luca CitiAbstract:Goal: This paper reports on a novel algorithm for the analysis of Electrodermal Activity (EDA) using methods of convex optimization. EDA can be considered as one of the most common observation channels of sympathetic nervous system Activity, and manifests itself as a change in electrical properties of the skin, such as skin conductance (SC). Methods: The proposed model describes SC as the sum of three terms: the phasic component, the tonic component, and an additive white Gaussian noise term incorporating model prediction errors as well as measurement errors and artifacts. This model is physiologically inspired and fully explains EDA through a rigorous methodology based on Bayesian statistics, mathematical convex optimization, and sparsity. Results: The algorithm was evaluated in three different experimental sessions to test its robustness to noise, its ability to separate and identify stimulus inputs, and its capability of properly describing the Activity of the autonomic nervous system in response to strong affective stimulation. Significance: Results are very encouraging, showing good performance of the proposed method and suggesting promising future applicability, e.g., in the field of affective computing.
Ki H. Chon - One of the best experts on this subject based on the ideXlab platform.
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using Electrodermal Activity to validate multilevel pain stimulation in healthy volunteers evoked by thermal grills
American Journal of Physiology-regulatory Integrative and Comparative Physiology, 2020Co-Authors: Hugo F Posadaquintero, Youngsun Kong, Kimberly Nguyen, Cara Tran, Luke Beardslee, Longtu Chen, Tiantian Guo, Xiaomei Cong, Bin Feng, Ki H. ChonAbstract:We have tested the feasibility of thermal grills, a harmless method to induce pain. The thermal grills consist of interlaced tubes that are set at cool or warm temperatures, creating a painful "illusion" (no tissue injury is caused) in the brain when the cool and warm stimuli are presented collectively. Advancement in objective pain assessment research is limited because the gold standard, the self-reporting pain scale, is highly subjective and only works for alert and cooperative patients. However, the main difficulty for pain studies is the potential harm caused to participants. We have recruited 23 subjects in whom we induced electric pulses and thermal grill (TG) stimulation. The TG effectively induced three different levels of pain, as evidenced by the visual analog scale (VAS) provided by the subjects after each stimulus. Furthermore, objective physiological measurements based on Electrodermal Activity showed a significant increase in levels as stimulation level increased. We found that VAS was highly correlated with the TG stimulation level. The TG stimulation safely elicited pain levels up to 9 out of 10. The TG stimulation allows for extending studies of pain to ranges of pain in which other stimuli are harmful.
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innovations in Electrodermal Activity data collection and signal processing a systematic review
Sensors, 2020Co-Authors: Hugo F Posadaquintero, Ki H. ChonAbstract:The Electrodermal Activity (EDA) signal is an electrical manifestation of the sympathetic innervation of the sweat glands. EDA has a history in psychophysiological (including emotional or cognitive stress) research since 1879, but it was not until recent years that researchers began using EDA for pathophysiological applications like the assessment of fatigue, pain, sleepiness, exercise recovery, diagnosis of epilepsy, neuropathies, depression, and so forth. The advent of new devices and applications for EDA has increased the development of novel signal processing techniques, creating a growing pool of measures derived mathematically from the EDA. For many years, simply computing the mean of EDA values over a period was used to assess arousal. Much later, researchers found that EDA contains information not only in the slow changes (tonic component) that the mean value represents, but also in the rapid or phasic changes of the signal. The techniques that have ensued have intended to provide a more sophisticated analysis of EDA, beyond the traditional tonic/phasic decomposition of the signal. With many researchers from the social sciences, engineering, medicine, and other areas recently working with EDA, it is timely to summarize and review the recent developments and provide an updated and synthesized framework for all researchers interested in incorporating EDA into their research.
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time varying analysis of Electrodermal Activity during exercise
PLOS ONE, 2018Co-Authors: Hugo F Posadaquintero, John P. Florian, Natasa Reljin, Craig Mills, Ian Mills, Jaci L Vanheest, Ki H. ChonAbstract:The Electrodermal Activity (EDA) is a useful tool for assessing skin sympathetic nervous Activity. Using spectral analysis of EDA data at rest, we have previously found that the spectral band which is the most sensitive to central sympathetic control is largely confined to 0.045 to 0.25 Hz. However, the frequency band associated with sympathetic control in EDA has not been studied for exercise conditions. Establishing the band limits more precisely is important to ensure the accuracy and sensitivity of the technique. As exercise intensity increases, it is intuitive that the frequencies associated with the autonomic dynamics should also increase accordingly. Hence, the aim of this study was to examine the appropriate frequency band associated with the sympathetic nervous system in the EDA signal during exercise. Eighteen healthy subjects underwent a sub-maximal exercise test, including a resting period, walking, and running, until achieving 85% of maximum heart rate. Both EDA and ECG data were measured simultaneously for all subjects. The ECG was used to monitor subjects’ instantaneous heart rate, which was used to set the experiment’s end point. We found that the upper bound of the frequency band (Fmax) containing the EDA spectral power significantly shifted to higher frequencies when subjects underwent prolonged low-intensity (Fmax ~ 0.28) and vigorous-intensity exercise (Fmax ~ 0.37 Hz) when compared to the resting condition. In summary, we have found shifting of the sympathetic dynamics to higher frequencies in the EDA signal when subjects undergo physical Activity.
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Electrodermal Activity is sensitive to cognitive stress under water
Frontiers in Physiology, 2018Co-Authors: Hugo F Posadaquintero, John P. Florian, Alvaro D Orjuelacanon, Ki H. ChonAbstract:When divers are at depth in water, the high pressure and low temperature alone can cause severe stress, challenging the human physiological control systems. The addition of cognitive stress, for example during a military mission, exacerbates the challenge. In these conditions, humans are more susceptible to autonomic imbalance. Reliable tools for the assessment of the autonomic nervous system (ANS) could be used as indicators of the relative degree of stress a diver is experiencing, which could reveal heightened risk during a mission. Electrodermal Activity (EDA), a measure of the changes in conductance at the skin surface due to sweat production, is considered a promising alternative for the non-invasive assessment of sympathetic control of the ANS. EDA is sensitive to stress of many kinds. Therefore, as a first step, we tested the sensitivity of EDA, in the time and frequency domains, specifically to cognitive stress during water immersion of the subject (albeit with their measurement finger dry for safety). The data from 14 volunteer subjects were used from the experiment. After a 4-min adjustment and baseline period after being immersed in water, subjects underwent the Stroop task, which is known to induce cognitive stress. The time-domain indices of EDA, skin conductance level (SCL) and non-specific skin conductance responses (NS.SCRs), did not change during cognitive stress, compared to baseline measurements. Frequency-domain indices of EDA, EDASymp (based on power spectral analysis) and TVSymp (based on time-frequency analysis), did significantly change during cognitive stress. This leads to the conclusion that EDA, assessed by spectral analysis, is sensitive to cognitive stress in water-immersed subjects, and can potentially be used to detect cognitive stress in divers.
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Power Spectral Density Analysis of Electrodermal Activity for Sympathetic Function Assessment.
Annals of Biomedical Engineering, 2016Co-Authors: Hugo F. Posada-quintero, John P. Florian, Alvaro D. Orjuela-cañón, T. Aljama-corrales, Sonia Charleston-villalobos, Ki H. ChonAbstract:Time-domain features of Electrodermal Activity (EDA), the measurable changes in conductance at the skin surface, are typically used to assess overall activation of the sympathetic system. These time domain features, the skin conductance level (SCL) and the nonspecific skin conductance responses (NS.SCRs), are consistently elevated with sympathetic nervous arousal, but highly variable between subjects. A novel frequency-domain approach to quantify sympathetic function using the power spectral density (PSD) of EDA is proposed. This analysis was used to examine if some of the induced stimuli invoke the sympathetic nervous system's dynamics which can be discernible as a large spectral peak, conjectured to be present in the low frequency band. The resulting indices were compared to the power of low-frequency components of heart rate variability (HRVLF) time series, as well as to time-domain features of EDA. Twelve healthy subjects were subjected to orthostatic, physical and cognitive stress, to test these techniques. We found that the increase in the spectral powers of the EDA was largely confined to 0.045-0.15 Hz, which is in the prescribed band for HRVLF. These low frequency components are known to be, in part, influenced by the sympathetic nervous dynamics. However, we found an additional 5-10% of the spectral power in the frequency range of 0.15-0.25 Hz with all three stimuli. Thus, dynamics of the normalized sympathetic component of the EDA, termed EDASympn, are represented in the frequency band 0.045-0.25 Hz; only a small amount of spectral power is present in frequencies higher than 0.25 Hz. Our results showed that the time-domain indices (the SCL and NS.SCRs), and EDASympn, exhibited significant increases under orthostatic, physical, and cognitive stress. However, EDASympn was more responsive than the SCL and NS.SCRs to the cold pressor stimulus, while the latter two were more sensitive to the postural and Stroop tests. Additionally, EDASympn exhibited an acceptable degree of consistency and a lower coefficient of variation compared to the time-domain features. Therefore, PSD analysis of EDA is a promising technique for sympathetic function assessment.