Physiological synchrony in EEG, electrodermal activity and heart rate reflects shared selective auditory attention

Interpersonal physiological synchrony (PS), or the similarity of physiological signals between individuals over time, may be used to detect attentionally engaging moments in time. We here investigated whether PS in the electroencephalogram (EEG), electrodermal activity (EDA), heart rate and a multimodal metric signals the occurrence of attentionally relevant events in time in two groups of participants. Both groups were presented with the same auditory stimulus, but were instructed to attend either to the narrative of an audiobook (audiobook-attending: AA group) or to interspersed emotional sounds and beeps (stimulus-attending: SA group). We hypothesized that emotional sounds could be detected in both groups as they are expected to draw attention involuntarily, in a bottom-up fashion. Indeed, we found this to be the case for PS in EDA or the multimodal metric. Beeps, that are expected to be only relevant due to specific “top-down” attentional instructions, could indeed only be detected using PS among SA participants, for EDA, EEG and the multimodal metric. We further hypothesized that moments in the audiobook accompanied by high PS in either EEG, EDA, heart rate or the multimodal metric for AA participants would be rated as more engaging by an independent group of participants compared to moments corresponding to low PS. This hypothesis was not supported. Our results show that PS can support the detection of attentionally engaging events over time. Currently, the relation between PS and engagement is only established for well-defined, interspersed stimuli, whereas the relation between PS and a more abstract self-reported metric of engagement over time has not been established. As the relation between PS and engagement is dependent on event type and physiological measure, we suggest to choose a measure matching with the stimulus of interest. When the stimulus type is unknown, a multimodal metric is most robust.

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Predicting Collaborative Learning Quality through Physiological Synchrony Recorded by Wearable Biosensors

10.1101/2020.06.01.127449 ◽

2020 ◽

Author(s):

Yang Liu ◽

Tingting Wang ◽

Kun Wang ◽

Yu Zhang

Keyword(s):

Heart Rate ◽

Data Analysis ◽

Collaborative Learning ◽

Electrodermal Activity ◽

Group Discussion ◽

Student Learn ◽

Classification Analysis ◽

Physiological Synchrony ◽

Collaboration Quality ◽

The Difference

AbstractInterpersonal physiological synchrony has been consistently found during collaborative tasks. However, few studies have applied synchrony to predict collaborative learning quality in real classroom. This study collected electrodermal activity (EDA) and heart rate (HR) in naturalistic class sessions, and compared the physiological synchrony between independent task and group discussion task. Since each student learn differently and not everyone prefers collaborative learning, participants were sorted into collaboration and independent dyads based on collaborative behaviors before data analysis. The result showed that during groups discussions, high collaboration pairs produced significantly higher synchrony than low collaboration dyads (p = 0.010). Given the equivalent engagement level during independent and collaborative tasks, the difference of physiological synchrony between high and low collaboration dyads was triggered by collaboration quality. Building upon this result, the classification analysis was conducted, indicating that EDA synchrony can predict collaboration quality (AUC = 0.767, p = 0.015).

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Unsupervised Clustering of Individuals Sharing Selective Attentional Focus Using Physiological Synchrony

Frontiers in Neuroergonomics ◽

10.3389/fnrgo.2021.750248 ◽

2022 ◽

Vol 2 ◽

Author(s):

Ivo V. Stuldreher ◽

Alexandre Merasli ◽

Nattapong Thammasan ◽

Jan B. F. van Erp ◽

Anne-Marie Brouwer

Keyword(s):

Heart Rate ◽

Real World ◽

Electrodermal Activity ◽

Clustering Algorithms ◽

Attentional Focus ◽

Unsupervised Clustering ◽

Limited Information ◽

Attentional State ◽

Sensory Stimuli ◽

Physiological Synchrony

Research on brain signals as indicators of a certain attentional state is moving from laboratory environments to everyday settings. Uncovering the attentional focus of individuals in such settings is challenging because there is usually limited information about real-world events, as well as a lack of data from the real-world context at hand that is correctly labeled with respect to individuals' attentional state. In most approaches, such data is needed to train attention monitoring models. We here investigate whether unsupervised clustering can be combined with physiological synchrony in the electroencephalogram (EEG), electrodermal activity (EDA), and heart rate to automatically identify groups of individuals sharing attentional focus without using knowledge of the sensory stimuli or attentional focus of any of the individuals. We used data from an experiment in which 26 participants listened to an audiobook interspersed with emotional sounds and beeps. Thirteen participants were instructed to focus on the narrative of the audiobook and 13 participants were instructed to focus on the interspersed emotional sounds and beeps. We used a broad range of commonly applied dimensionality reduction ordination techniques—further referred to as mappings—in combination with unsupervised clustering algorithms to identify the two groups of individuals sharing attentional focus based on physiological synchrony. Analyses were performed using the three modalities EEG, EDA, and heart rate separately, and using all possible combinations of these modalities. The best unimodal results were obtained when applying clustering algorithms on physiological synchrony data in EEG, yielding a maximum clustering accuracy of 85%. Even though the use of EDA or heart rate by itself did not lead to accuracies significantly higher than chance level, combining EEG with these measures in a multimodal approach generally resulted in higher classification accuracies than when using only EEG. Additionally, classification results of multimodal data were found to be more consistent across algorithms than unimodal data, making algorithm choice less important. Our finding that unsupervised classification into attentional groups is possible is important to support studies on attentional engagement in everyday settings.

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Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review

Sensors ◽

10.3390/s21103461 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3461

Author(s):

Blake Anthony Hickey ◽

Taryn Chalmers ◽

Phillip Newton ◽

Chin-Teng Lin ◽

David Sibbritt ◽

...

Keyword(s):

Mental Health ◽

Systematic Review ◽

Heart Rate ◽

Heart Rate Variability ◽

Electrodermal Activity ◽

Smart Devices ◽

Stress Detection ◽

Wearable Technologies ◽

Detection Of Stress ◽

Anxiety Depression

Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.

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Quantifying Occupational Stress in Intensive Care Unit Nurses: An Applied Naturalistic Study of Correlations Among Stress, Heart Rate, Electrodermal Activity, and Skin Temperature

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/00187208211040889 ◽

2021 ◽

pp. 001872082110408

Author(s):

Nima Ahmadi ◽

Farzan Sasangohar ◽

Tariq Nisar ◽

Valerie Danesh ◽

Ethan Larsen ◽

...

Keyword(s):

Intensive Care Unit ◽

Heart Rate ◽

Intensive Care ◽

Skin Temperature ◽

Electrodermal Activity ◽

Laboratory Research ◽

Prior Literature ◽

High Level ◽

Intensive Care Unit Nurses ◽

Skin Temperatures

Objective To identify physiological correlates to stress in intensive care unit nurses. Background Most research on stress correlates are done in laboratory environments; naturalistic investigation of stress remains a general gap. Method Electrodermal activity, heart rate, and skin temperatures were recorded continuously for 12-hr nursing shifts (23 participants) using a wrist-worn wearable technology (Empatica E4). Results Positive correlations included stress and heart rate (ρ = .35, p < .001), stress and skin temperature (ρ = .49, p < .05), and heart rate and skin temperatures (ρ = .54, p = .0008). Discussion The presence and direction of some correlations found in this study differ from those anticipated from prior literature, illustrating the importance of complementing laboratory research with naturalistic studies. Further work is warranted to recognize nursing activities associated with a high level of stress and the underlying reasons associated with changes in physiological responses. Application Heart rate and skin temperature may be used for real-time detection of stress, but more work is needed to validate such surrogate measures.

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Trends in Heart-Rate Variability Signal Analysis

Frontiers in Digital Health ◽

10.3389/fdgth.2021.639444 ◽

2021 ◽

Vol 3 ◽

Author(s):

Syem Ishaque ◽

Naimul Khan ◽

Sri Krishnan

Keyword(s):

Machine Learning ◽

Heart Rate ◽

Heart Rate Variability ◽

Electrodermal Activity ◽

Well Being ◽

Good Health ◽

Machine Learning Techniques ◽

Video Gaming ◽

Clinical Events ◽

Learning Techniques

Heart rate variability (HRV) is the rate of variability between each heartbeat with respect to time. It is used to analyse the Autonomic Nervous System (ANS), a control system used to modulate the body's unconscious action such as cardiac function, respiration, digestion, blood pressure, urination, and dilation/constriction of the pupil. This review article presents a summary and analysis of various research works that analyzed HRV associated with morbidity, pain, drowsiness, stress and exercise through signal processing and machine learning methods. The points of emphasis with regards to HRV research as well as the gaps associated with processes which can be improved to enhance the quality of the research have been discussed meticulously. Restricting the physiological signals to Electrocardiogram (ECG), Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) analysis resulted in 25 articles which examined the cause and effect of increased/reduced HRV. Reduced HRV was generally associated with increased morbidity and stress. High HRV normally indicated good health, and in some instances, it could signify clinical events of interest such as drowsiness. Effective analysis of HRV during ambulatory and motion situations such as exercise, video gaming, and driving could have a significant impact toward improving social well-being. Detection of HRV in motion is far from perfect, situations involving exercise or driving reported accuracy as high as 85% and as low as 59%. HRV detection in motion can be improved further by harnessing the advancements in machine learning techniques.

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Quantitative assessment of the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousness

10.1101/2020.11.03.367607 ◽

2020 ◽

Author(s):

Sandya Subramanian ◽

Patrick L. Purdon ◽

Riccardo Barbieri ◽

Emery N. Brown

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Prediction Models ◽

Electrodermal Activity ◽

Strong Relationship ◽

Pulse Amplitude ◽

Physiological Data ◽

Statistical Tool ◽

Autonomic Changes ◽

Before And After

ABSTRACTDuring general anesthesia, both behavioral and autonomic changes are caused by the administration of anesthetics such as propofol. Propofol produces unconsciousness by creating highly structured oscillations in brain circuits. The anesthetic also has autonomic effects due to its actions as a vasodilator and myocardial depressant. Understanding how autonomic dynamics change in relation to propofol-induced unconsciousness is an important scientific and clinical question since anesthesiologists often infer changes in level of unconsciousness from changes in autonomic dynamics. Therefore, we present a framework combining physiology-based statistical models that have been developed specifically for heart rate variability and electrodermal activity with a robust statistical tool to compare behavioral and multimodal autonomic changes before, during, and after propofol-induced unconsciousness. We tested this framework on physiological data recorded from nine healthy volunteers during computer-controlled administration of propofol. We studied how autonomic dynamics related to behavioral markers of unconsciousness: 1) overall, 2) during the transitions of loss and recovery of consciousness, and 3) before and after anesthesia as a whole. Our results show a strong relationship between behavioral state of consciousness and autonomic dynamics. All of our prediction models showed areas under the curve greater than 0.75 despite the presence of non-monotonic relationships among the variables during the transition periods. Our analysis highlighted the specific roles played by fast versus slow changes, parasympathetic vs sympathetic activity, heart rate variability vs electrodermal activity, and even pulse rate vs pulse amplitude information within electrodermal activity. Further advancement upon this work can quantify the complex and subject-specific relationship between behavioral changes and autonomic dynamics before, during, and after anesthesia. However, this work demonstrates the potential of a multimodal, physiologically-informed, statistical approach to characterize autonomic dynamics.

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The Three Arousal Model: Implications of Gray's Two-Factor Learning Theory for Heart Rate, Electrodermal Activity, and Psychopathy

Psychophysiology ◽

10.1111/j.1469-8986.1980.tb00117.x ◽

1980 ◽

Vol 17 (2) ◽

pp. 87-104 ◽

Cited By ~ 717

Author(s):

Don C. Fowles

Keyword(s):

Heart Rate ◽

Learning Theory ◽

Electrodermal Activity ◽

Arousal Model

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Five New Things: Seizure Detection Devices

Neurology Clinical Practice ◽

10.1212/cpj.0000000000001044 ◽

2021 ◽

pp. 10.1212/CPJ.0000000000001044

Author(s):

Alexandra Carrick Atwood ◽

Cornelia Natasha Drees

Keyword(s):

Heart Rate ◽

Motion Detection ◽

Electrodermal Activity ◽

False Positive Rate ◽

Seizure Detection ◽

Mechanisms Of Action ◽

Unexpected Death ◽

Positive Rate ◽

Convulsive Seizures ◽

Electroencephalogram Eeg

ABSTRACT:Purpose: The purpose of this paper is to review seizure detection devices, their mechanisms of action, efficacy and reflecting upon potential improvements for future devices.Recent Findings: There are five main categories of seizure detection devices ([email protected]), these include electroencephalogram (EEG), heart rate detection (HR), electrodermal activity (EDA), motion detection and electromyography (EMG). These devices can be used in combination or in isolation to detect seizures. These devices are high in their sensitivity for convulsive seizures, but low in specificity because of a tendency to detect artifact. Overall, they perform poorly identifying non-convulsive seizures.Summary: Seizure detection devices are currently most useful in detecting convulsive seizures and thereby might help against sudden unexpected death in epilepsy (SUDEP), though they have a high false positive rate. These devices are much less adept at detecting more clinically subtle seizures.

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Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning

Education and Information Technologies ◽

10.1007/s10639-019-10059-5 ◽

2019 ◽

Vol 25 (3) ◽

pp. 1785-1802 ◽

Cited By ~ 4

Author(s):

Muhterem Dindar ◽

Jonna Malmberg ◽

Sanna Järvelä ◽

Eetu Haataja ◽

Paul A. Kirschner

Keyword(s):

Academic Achievement ◽

Electrodermal Activity ◽

Learning Processes ◽

Self Report ◽

Self Regulated Learning ◽

Cognitive Regulation ◽

Physiological Synchrony ◽

Regulated Learning ◽

Report Data ◽

Self Report Data

AbstractThis study investigated the interplay of temporal changes in self-regulated learning processes (i.e., behavioral, cognitive, motivational and emotional) and their relationship with academic achievement in computer-supported collaborative learning. The study employed electrodermal activity and self-report data to capture the dynamicity of self-regulated learning processes during 15 sessions of collaborative learning activities. Our findings revealed that the changes in motivational regulation was related to academic achievement. However, academic achievement was not related to behavioral regulation, cognitive regulation or emotional regulation. Physiological synchrony among the collaborating students was found to be related only to cognitive regulation. The results also showed that the concordance of self-report data among the collaborating students was related to higher physiological synchrony among them in the behavioral, cognitive, and motivational dimensions of self-regulated learning. The findings reflect the complexity of the relationships between self-regulated learning constructs and demonstrates the potential value of physiological measures in self-regulated learning research.

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