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 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.

scholarly journals Conscious processing of narrative stimuli synchronizes heart rate between individuals

2020 ◽  
Author(s):  
Pauline Pérez ◽  
Jens Madsen ◽  
Leah Banellis ◽  
Başak Türker ◽  
Federico Raimondo ◽  
...  
Keyword(s):  
Heart Rate ◽  
Autonomic Function ◽  
Conscious State ◽  
Attentional Focus ◽  
Disorders Of Consciousness ◽  
Attentional State ◽  
Healthy Individuals ◽  
Neural Processes ◽  
Heart Rate Fluctuations ◽  
Conscious Processing

AbstractHeart rate has natural fluctuations that are typically ascribed to autonomic function. Recent evidence suggests that conscious processing can affect the timing of the heartbeat. We hypothesized that heart rate is modulated by conscious processing and therefore dependent on attentional focus. To test this, we leverage the observation that neural processes can be synchronized between subjects by presenting an identical narrative stimulus. As predicted, we find significant inter-subject correlation of the heartbeat (ISC-HR) when subjects are presented with an auditory or audiovisual narrative. Consistent with the conscious processing hypothesis, we find that ISC-HR is reduced when subjects are distracted from the narrative, and that higher heart rate synchronization predicts better recall of the narrative. Finally, patients with disorders of consciousness who are listening to a story have lower ISC-HR, as compared to healthy individuals, and that individual ISC-HR might predict a patients’ prognosis.. We conclude that heart rate fluctuations are partially driven by conscious processing, depend on attentional state, and may represent a simple metric to assess conscious state in unresponsive patients.

Benchmarking the Use of Immersive Virtual Bike Simulators for Understanding Cyclist Behaviors

2021 ◽  
Author(s):  
Xiang Guo ◽  
Erin Marie Robartes ◽  
Austin Angulo ◽  
T. Donna Chen ◽  
Arsalan Heydarian
Keyword(s):  
Heart Rate ◽  
Real World ◽  
Environmental Changes ◽  
Low Cost ◽  
Real Life ◽  
Limited Information ◽  
Perceived Safety ◽  
Realistic Representation ◽  
Human Sensing ◽  
The Impact

Recent reports indicate that cyclist fatalities are rising. Unlike automobile driver crash and safety studies, there is very limited information and data on how different environmental or design features impact cyclists’ behaviors, attention, and awareness. Real world studies evaluating cyclist behavior are limited due to their inherent safety risk; therefore, there is a need for alternate data to better inform the planning and design of roadways for all users. Immersive virtual environments (IVE) have shown to provide a realistic representation of real-world conditions; however, these tools have not been evaluated and validated for vulnerable road users, such as cyclists. The purpose of this study is to assess the use of an IVE bike simulator to study the impact of design and environmental conditions on cyclists’ perceived safety and behavioral changes. By benchmarking cyclists' behaviors and perceived safety in real-life settings compared to its representative IVE bike simulation, we can validate whether these IVE simulators are realistic representations of real-world conditions. Furthermore, by connecting these environments with the latest low-cost human sensing devices, we have built a multimodal human sensing data collection system to track participants’ gaze, heart rate, and head movement. The preliminary results from a six-participant pilot study indicate that our simulators are capable of replicating cyclists’ speed profile, heart rate changes, and most of the head and gaze behaviors and that these measurements are sensitive to environmental changes.

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).

Heart Rate Variability, Homeostasis, and Brain Function

2012 ◽  
Vol 26 (4) ◽  
pp. 178-203 ◽  
Author(s):  
Francesco Riganello ◽  
Sergio Garbarino ◽  
Walter G. Sannita
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Communication Disorders ◽  
Affective States ◽  
Experimental Conditions ◽  
Cardiovascular Research ◽  
Sensory Stimuli ◽  
Additional Information ◽  
Brain Injured

Measures of heart rate variability (HRV) are major indices of the sympathovagal balance in cardiovascular research. These measures are thought to reflect complex patterns of brain activation as well and HRV is now emerging as a descriptor thought to provide information on the nervous system organization of homeostatic responses in accordance with the situational requirements. Current models of integration equate HRV to the affective states as parallel outputs of the central autonomic network, with HRV reflecting its organization of affective, physiological, “cognitive,” and behavioral elements into a homeostatic response. Clinical application is in the study of patients with psychiatric disorders, traumatic brain injury, impaired emotion-specific processing, personality, and communication disorders. HRV responses to highly emotional sensory inputs have been identified in subjects in vegetative state and in healthy or brain injured subjects processing complex sensory stimuli. In this respect, HRV measurements can provide additional information on the brain functional setup in the severely brain damaged and would provide researchers with a suitable approach in the absence of conscious behavior or whenever complex experimental conditions and data collection are impracticable, as it is the case, for example, in intensive care units.

scholarly journals A novel digital approach to describe real world outcomes among patients with constipation

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Allison Shapiro ◽  
Benjamin Bradshaw ◽  
Sabine Landes ◽  
Petra Kammann ◽  
Beatrice Bois De Fer ◽  
...  
Keyword(s):  
Heart Rate ◽  
Prospective Study ◽  
Real World ◽  
Symptom Severity ◽  
Interaction Analysis ◽  
Behavioral Activity ◽  
Patient Centered ◽  
Small Magnitude ◽  
Digital Devices ◽  
Bowel Movements

AbstractUnderstanding day-to-day variations in symptoms and medication management can be important in describing patient centered outcomes for people with constipation. Patient Generated Health Data (PGHD) from digital devices is a potential solution, but its utility as a tool for describing experiences of people with frequent constipation is unknown. We conducted a virtual, 16-week prospective study of individuals with frequent constipation from an online wellness platform that connects mobile consumer digital devices including wearable monitors capable of passively collecting steps, sleep, and heart rate data. Participants wore a Fitbit monitoring device for the study duration and were administered daily and monthly surveys assessing constipation symptom severity and medication usage. A set of 38 predetermined day-level behavioral activity metrics were computed from minute-level data streams for steps, sleep and heart rate. Mixed effects regression models were used to compare activity metrics between constipation status (irregular or constipated vs. regular day), medication use (medication day vs. non-medication day) and the interaction of medication day with irregular or constipation days, as well as to model likelihood to treat with constipation medications based on daily self-reported symptom severity. Correction for multiple comparisons was performed with the Benjamini–Hochberg procedure for false discovery rate. This study analyzed 1540 enrolled participants with completed daily surveys (mean age 36.6 sd 10.0, 72.8% female, 88.8% Caucasian). Of those, 1293 completed all monthly surveys and 756 had sufficient Fitbit data density for analysis of activity metrics. At a daily-level, 22 of the 38 activity metrics were significantly associated with bowel movement or medication treatment patterns for constipation. Participants were measured to have fewer steps on irregular days compared to regular days (−200 steps, 95% CI [−280, −120]), longer periods of inactivity on constipated days (9.1 min, 95% CI [5.2, 12.9]), reduced total sleep time on irregular and constipated days (−2.4 min, 95% CI [−4.3, −0.4] and −4.0 min, 95% CI [−6.5, −1.4], respectively). Participants reported greater severity of symptoms for bloating, hard stool, difficulty passing, and painful bowel movements on irregular, constipation and medication days compared to regular days with no medication. Interaction analysis of medication days with irregular or constipation days observed small increases in severity compared to non-medication days. Participants were 4.3% (95% CI 3.2, 5.3) more likely to treat with medication on constipated days versus regular. No significant increase in likelihood was observed for irregular days. Daily likelihood to treat increased for each 1-point change in symptom severity of bloating (2.4%, 95% CI [2.0, 2.7]), inability to pass (2.2%, 95% CI [1.4, 3.0]) and incomplete bowel movements (1.3%, 95% CI [0.9, 1.7]). This is the first large scale virtual prospective study describing the association between passively collected PGHD and constipation symptoms and severity at a day-to-day granularity level. Constipation status, irregular or constipated, was associated with a number of activity metrics in steps and sleep, and likelihood to treat with medication increased with increasing severity for a number of constipation symptoms. Given the small magnitude of effect, further research is needed to understand the clinical relevance of these results. PGHD may be useful as a tool for describing real world patient centered experiences for people with constipation.

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 Performance in complex life situations: effects of age, cognition, and walking speed in virtual versus real life environments

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Michal Kafri ◽  
Patrice L. Weiss ◽  
Gabriel Zeilig ◽  
Moshe Bondi ◽  
Ilanit Baum-Cohen ◽  
...  
Keyword(s):  
Older Adults ◽  
Heart Rate ◽  
Virtual Environment ◽  
Real World ◽  
Age Groups ◽  
Age Effects ◽  
Shopping Mall ◽  
Complex Task ◽  
The Real ◽  
Gait Parameters

Abstract Background Virtual reality (VR) enables objective and accurate measurement of behavior in ecologically valid and safe environments, while controlling the delivery of stimuli and maintaining standardized measurement protocols. Despite this potential, studies that compare virtual and real-world performance of complex daily activities are scarce. This study aimed to compare cognitive strategies and gait characteristics of young and older healthy adults as they engaged in a complex task while navigating in a real shopping mall and a high-fidelity virtual replica of the mall. Methods Seventeen older adults (mean (SD) age = 71.2 (5.6) years, 64% males) and 17 young adults (26.7 (3.7) years, 35% males) participated. In two separate sessions they performed the Multiple Errands Test (MET) in a real-world mall or the Virtual MET (VMET) in the virtual environment. The real-world environment was a small shopping area and the virtual environment was created within the CAREN™ (Computer Assisted Rehabilitation Environment) Integrated Reality System. The performance of the task was assessed using motor and physiological measures (gait parameters and heart rate), MET or VMET time and score, and navigation efficiency (cognitive performance and strategy). Between (age groups) and within (environment) differences were analyzed with ANOVA repeated measures. Results There were no significant age effects for any of the gait parameters but there were significant environment effects such that both age groups walked faster (F(1,32) = 154.96, p < 0.0001) with higher step lengths (F(1,32) = 86.36, p < 0.0001), had lower spatial and temporal gait variability (F(1,32) = 95.71–36.06, p < 0.0001) and lower heart rate (F(1,32) = 13.40, p < 0.01) in the real-world. There were significant age effects for MET/VMET scores (F(1,32) = 19.77, p < 0.0001) and total time (F(1,32) = 11.74, p < 0.05) indicating better performance of the younger group, and a significant environment effect for navigation efficiency (F(1,32) = 7.6, p < 0.01) that was more efficient in the virtual environment. Conclusions This comprehensive, ecological approach in the measurement of performance during tasks reminiscent of complex life situations showed the strengths of using virtual environments in assessing cognitive aspects and limitations of assessing motor aspects of performance. Difficulties by older adults were apparent mainly in the cognitive aspects indicating a need to evaluate them during complex task performance.

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