scholarly journals Physiological Synchrony in EEG, Electrodermal Activity and Heart Rate Detects Attentionally Relevant Events in Time

2020 ◽  
Vol 14 ◽  
Author(s):  
Ivo V. Stuldreher ◽  
Nattapong Thammasan ◽  
Jan B. F. van Erp ◽  
Anne-Marie Brouwer
Keyword(s):  
Heart Rate ◽  
Stimulus Type ◽  
Electrodermal Activity ◽  
Physiological Signals ◽  
Independent Group ◽  
Top Down ◽  
Event Type ◽  
Physiological Synchrony ◽  
Electroencephalogram Eeg ◽  
Over Time

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.

Five New Things: Seizure Detection Devices

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.

scholarly journals Evaluating physiological signal salience for estimating metabolic energy cost from wearable sensors

2019 ◽  
Vol 126 (3) ◽  
pp. 717-729 ◽  
Author(s):  
Kimberly A. Ingraham ◽  
Daniel P. Ferris ◽  
C. David Remy
Keyword(s):  
Heart Rate ◽  
Steady State ◽  
Indirect Calorimetry ◽  
Energy Cost ◽  
Electrodermal Activity ◽  
Wearable Sensors ◽  
Metabolic Energy ◽  
Physiological Signals ◽  
Physiological Signal ◽  
Signal Salience

Body-in-the-loop optimization algorithms have the capability to automatically tune the parameters of robotic prostheses and exoskeletons to minimize the metabolic energy expenditure of the user. However, current body-in-the-loop algorithms rely on indirect calorimetry to obtain measurements of energy cost, which are noisy, sparsely sampled, time-delayed, and require wearing a respiratory mask. To improve these algorithms, the goal of this work is to predict a user’s steady-state energy cost quickly and accurately using physiological signals obtained from portable, wearable sensors. In this paper, we quantified physiological signal salience to discover which signals, or groups of signals, have the best predictive capability when estimating metabolic energy cost. We collected data from 10 healthy individuals performing 6 activities (walking, incline walking, backward walking, running, cycling, and stair climbing) at various speeds or intensities. Subjects wore a suite of physiological sensors that measured breath frequency and volume, limb accelerations, lower limb EMG, heart rate, electrodermal activity, skin temperature, and oxygen saturation; indirect calorimetry was used to establish the ‘ground truth’ energy cost for each activity. Evaluating Pearson’s correlation coefficients and single and multiple linear regression models with cross validation (leave-one- subject-out and leave-one- task-out), we found that 1) filtering the accelerations and EMG signals improved their predictive power, 2) global signals (e.g., heart rate, electrodermal activity) were more sensitive to unknown subjects than tasks, while local signals (e.g., accelerations) were more sensitive to unknown tasks than subjects, and 3) good predictive performance was obtained combining a small number of signals (4–5) from multiple sensor modalities. NEW & NOTEWORTHY In this paper, we systematically compare a large set of physiological signals collected from portable sensors and determine which sensor signals contain the most salient information for predicting steady-state metabolic energy cost, robust to unknown subjects or tasks. This information, together with the comprehensive data set that is published in conjunction with this paper, will enable researchers and clinicians across many fields to develop novel algorithms to predict energy cost from wearable sensors.

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

2020 ◽  
Vol 17 (4) ◽  
pp. 046028 ◽  
Author(s):  
Ivo V Stuldreher ◽  
Nattapong Thammasan ◽  
Jan B F van Erp ◽  
Anne-Marie Brouwer
Keyword(s):  
Heart Rate ◽  
Electrodermal Activity ◽  
Auditory Attention ◽  
Physiological Synchrony

scholarly journals Predicting Collaborative Learning Quality through Physiological Synchrony Recorded by Wearable Biosensors

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).

scholarly journals Unsupervised Clustering of Individuals Sharing Selective Attentional Focus Using Physiological Synchrony

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.

scholarly journals An Investigation of the Physical and Electrical Properties of Knitted Electrodes When Subjected to Multi-Axial Compression and Abrasion

Proceedings ◽  
2021 ◽  
Vol 68 (1) ◽  
pp. 2
Author(s):  
Arash M. Shahidi ◽  
Theodore Hughes-Riley ◽  
Carlos Oliveira ◽  
Tilak Dias
Keyword(s):  
Heart Rate ◽  
Electrical Properties ◽  
Applied Load ◽  
Pressure Sensors ◽  
Electrical Performance ◽  
Electronic Textiles ◽  
Heart Rate Monitors ◽  
Mechanical Loads ◽  
And Performance ◽  
Over Time

Knitted electrodes are a key component to many electronic textiles including sensing devices, such as pressure sensors and heart rate monitors; therefore, it is essential to assess the electrical performance of these knitted electrodes under different mechanical loads to understand their performance during use. The electrical properties of the electrodes could change while deforming, due to an applied load, which could occur in the uniaxial direction (while stretched) or multiaxial direction (while compressed). The properties and performance of the electrodes could also change over time when rubbed against another surface due to the frictional force and generated heat. This work investigates the behavior of a knitted electrode under different loading conditions and after multiple abrasion cycles.

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

Sensors ◽  
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.

scholarly journals Ordering global governance complexes: The evolution of the governance complex for international civil aviation

2021 ◽  
Author(s):  
Mette Eilstrup-Sangiovanni
Keyword(s):  
Global Governance ◽  
International Institutions ◽  
Historical Data ◽  
Civil Aviation ◽  
Top Down ◽  
Bottom Up ◽  
Governance Systems ◽  
Governance Effectiveness ◽  
Political Agents ◽  
Over Time

AbstractMany observers worry that growing numbers of international institutions with overlapping functions undermine governance effectiveness via duplication, inconsistency and conflict. Such pessimistic assessments may undervalue the mechanisms available to states and other political agents to reduce conflictual overlap and enhance inter-institutional synergy. Drawing on historical data I examine how states can mitigate conflict within Global Governance Complexes (GGCs) by dissolving or merging existing institutions or by re-configuring their mandates. I further explore how “order in complexity” can emerge through bottom-up processes of adaptation in lieu of state-led reform. My analysis supports three theoretical claims: (1) states frequently refashion governance complexes “top-down” in order to reduce conflictual overlap; (2) “top-down” restructuring and “bottom-up” adaptation present alternative mechanisms for ordering relations among component institutions of GGCs; (3) these twin mechanisms ensure that GGCs tend to (re)produce elements of order over time–albeit often temporarily. Rather than evolving towards ever-greater fragmentation and disorder, complex governance systems thus tend to fluctuate between greater or lesser integration and (dis)order.

Precarity Unbound

2007 ◽  
Vol 32 (3) ◽  
pp. 319-340 ◽  
Author(s):  
Nancy Ettlinger
Keyword(s):  
Everyday Life ◽  
Final Section ◽  
Multiple Scales ◽  
Bush Administration ◽  
Radical Democracy ◽  
Top Down ◽  
Hardt And Negri ◽  
Global Events ◽  
Time Periods ◽  
Over Time

Departing from tendencies to bound precarity in particular time periods and world regions, this article develops an expansive view of precarity over time and across space. Beyond effects of specific global events and macroscale structures, precarity inhabits the microspaces of everyday life. However, people attempt to disengage the stress of precarious life by constructing the illusion of certainty. Reflexive denial of precarious life entails essentialist strategies that implicitly or explicitly classify and homogenize people and phenomena, legitimize the constructed boundaries, and in the process aim at eliminating difference and possibilities for negotiation; the tension between these goals and material realities helps explain misrepresentations that can be catastrophic at multiple scales, re-creating precarity. Reactions to 9/11 by the Bush administration represent a case in point of reflexive denial of precarity through strategies that created illusions of certainty with deleterious results. Normatively, the paradox of precarious life and reflexive denials prompts questions as to how urges for certainty in the context of precarity might be constructively channeled. the author approaches this challenge in the final section by drawing from a nexus of concerns about post-Habermasian radical democracy, individual thought and feeling, and network dynamics. Whereas Hardt and Negri reverse the direction of the Foucauldian concept of biopower from top-down to bottom-up, the author draws from Foucault's concept of governmentality in relation to resistance to imagine a cooperative politics operating within as well as across scales.

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