GREENVEC GAME FOR SKIN CONDUCTIVITY LEVEL (SCL) BIOFEEDBACK PERFORMANCE SIMULATOR USING GALVANIC SKIN RESPONSE (GSR) SENSOR

Stress is induced in response to any mental, physical or emotional change associated with our daily experiences. While short term stress can be quite beneficial, prolonged stress is detrimental to the heart, muscle tissues and immune system. In order to be proactive against these symptoms, it is important to assess the impact of stress due to various activities, which is initially determined through the change in the sympathetic (SNS) and parasympathetic (PNS) nervous systems. After acquiring physiological data wirelessly through captive electrocardiogram (ECG), galvanic skin response (GSR) and respiration (RESP) sensors, 21 time, frequency, nonlinear, GSR and respiration features were manually extracted from 15 subjects ensuing a baseline phase, virtual reality (VR) roller coaster simulation, color Stroop task and VR Bubble Bloom game. This paper presents a comprehensive physiological analysis of stress from an experiment involving a VR video game Bubble Bloom to manage stress levels. A personalized classification and regression tree (CART) model was developed using a novel Gini index algorithm in order to effectively classify binary classes of stress. A novel K-means feature was derived from 11 other features and used as an input in the Decision Tree (DT) algorithm, strong learners Ensemble Gradient Boosting (EGB) and Extreme Gradient Boosting (XGBoost (XGB)) embedded in a pipeline to classify 5 classes of stress. Results obtained indicate that heart rate (HR), approximate entropy (ApEN), low frequency and high frequency ratio (LF/HF), low frequency (LF), standard deviation (SD1), GSR and RESP all reduced and high frequency (HF) increased following the VR Bubble Bloom game phase. The personalized CART model was able to classify binary stress with 87.75% accuracy. It proved to be more effective than other related studies. EGB was able to classify binary stress with 100% accuracy, which outperformed every other related study. XGBoost and DT were able to classify five classes of stress with 72.22% using the novel K-means feature. This feature produced less error and better model performance in comparison to using all the features. Results substantiate that our proposed methods were more effective for stress classification than most related studies.

Flexible galvanic skin response sensor based on vertically aligned silver nanowires

Biosensing technologies, which monitor physiological responses such as Heart Rate Variability (HRL), Skin Conductance Level (SCL), and Electroencephalogram (EEG) activity, offer a novel approach to enhancing the dissemination of health information in ideological and political education (IPE). In this context, health information encompasses topics such as mental health, stress management, and healthy lifestyle practices, all crucial to students’ overall well-being. Traditional health education methods cannot often capture real-time physiological and emotional responses, which can improve engagement and learning outcomes. This research explores the effectiveness of biosensing technology in enhancing the dissemination of health information within IPE. It examines how physiological data can be utilized to assess student engagement, emotional responses, and learning outcomes related to health. A mixed-methods approach was adopted, combining quantitative data from wearable biosensors (heart rate monitors, Galvanic Skin Response (GSR) sensors, EEG headsets) with qualitative feedback from students. Physiological data were preprocessed using signal filtering techniques, such as the Savitzky-Golay Filter, and features such as heart rate variability, skin conductance, and EEG alpha waves were extracted using the Kalman Filter (KF). A Modified Runge-Kutta Optimizer Integrated with Deep Belief Networks (MRKO-DBN) classifier was employed to predict student engagement based on these features. The research revealed that physiological responses, particularly heart rate variability and skin conductance, were strongly correlated with student engagement. The MRKO-DBN model achieved accuracy in predicting engagement. Qualitative feedback further confirmed that Biosensing technology significantly improved students’ engagement. Integrating Biosensing technology into health education within ideological and political contexts offers significant potential for enhancing student engagement and learning outcomes. By providing real-time, personalized feedback, it fosters a more interactive and responsive learning environment.

How does haptic feedback during human-virtual character interaction affect participant physiological responses in virtual reality? In this between-subjects study, haptic feedback and non-haptic feedback conditions in which virtual characters bump into the participant who is immersed in a virtual environment are compared. A questionnaire was developed to determine the influence of haptic feedback on presence, embodiment, positive and negative affect, interaction with virtual characters, and haptic feedback realism, among other more exploratory concepts. These exploratory variables include engagement, flow, comfort with virtual characters, comfort with virtual characters’ appearance, realism of virtual character interaction, realism of haptic feedback, and virtual reality sickness. Physiological data was collected using galvanic skin response (GSR) to investigate the influence of haptic feedback on physiological arousal during human-virtual character interaction. Five conditions were developed (no haptic feedback, full and half intensity, incorrect position, and delayed timing). Significant differences were found in embodiment, realism of virtual character interaction, haptic feedback realism, and GSR amplitude after the first interaction with the virtual character. These results may inform future virtual reality studies that investigate haptic feedback during human-virtual character interaction and/or arousal via GSR data, as well as advise studies that seek to correlate self-report responses with physiological data.

Pre-exam anxiety is a very common feeling among university students that manifests at cognitive, behavioural, and physiological levels. It affects between 15 and 25% of students, generating avoidance behaviours towards evaluation, which can result in dropping out of school. The development of biofeedback-based devices that relay visual and auditory feedback on physiological symptoms, offers new options for individual programmes. Said devices can help a person acquire greater control over the bodily function that is being monitored and are currently being used in many fields. It is therefore worth assessing the effectiveness of this technology at processing information on the objective physiological response to anxiety in study situations. Furthermore, universities could develop health programmes implementing this technology to improve students’ quality of life. During the biofeedback sessions, the aim is to collect physiological measures of the anxiety response before and after two independent preventive psychological programmes. It is also intended to assess the presence of differences between physiological and subjective data through questionnaires with Likert-type scales. Two experimental groups were formed for this quasi-experimental applied research: G1 (19 students), Students participating in a Self-Control and Study Techniques Programme; and G2 (12 students), Students participating in the Test Anxiety Control Programme. Their objective physiological responses were evaluated in two biofeedback sessions: pre-test (before the start of the programmes) and post-test (after their completion) (4 weeks for G1 and 12 weeks for G2). The biofeedback device used was Neurobit Optima 4 + BT. The inclusion criteria were: (1) age of majority (18 years); (2) voluntary participation in the programs offered by the University Health Care Service of the University of Burgos; (3) signing the informed consent form. A P-value ≤ 0.05 was considered statistically significant. Twenty-two adults divided into G1 (12 students, 1 man and 11 women) and G2 (10 students, 1 men and 9 women) completed the programmes. The mean age and Standard Deviation (SD) of G1 and G2 were, respectively, 24.20 (6.93) years old and 25.00 (6.22) years old. There were significant temporal differences between measurement sessions (all P < 0.045). G1 presented significantly different galvanic skin responses (all P < 0.011) that tended to peak; G2 presented significantly different muscular tension and galvanic skin responses (all P < 0.037), both in total and by phases, showing progressively lower muscular tension, and galvanic skin responses that tended to peak. In G1, significant links were noted both between the items of the Test Anxiety Inventory (TAI) and the galvanic skin responses monitored during the pre-test session (r = 0.229; P = 0.012) and between the items of the Test Anxiety Inventory and the galvanic skin responses monitored during the post-test data-collection session (r = 0.201; P = 0.028). In G2, significant links were noted between the State-Trait Anxiety Inventory items and both muscular tension (r = 0.278; P = 0.005) and the galvanic skin response in the pre-test data-collection session (r = 0.258; P = 0.010) and between the TAI items and the galvanic skin response in the post-test data-collection session (r = − 0.232; P = 0.020). Most participants reported useful and interesting biofeedback experiences. Promising preliminary evidence is advanced in this study on the effectiveness of biofeedback as a tool for monitoring and relaying objective physiological data to participants in real time. The significance of these results should be examined in future research in which the emotional reactions of students facing test-related anxiety may be monitored for the subsequent development of therapeutic programmes.

Biofeedback combined with cue-exposure as a treatment for heroin addicts

Introduction: Virtual-reality (VR) technology has, over the last decade, quickly expanded from gaming into other sectors including training, education, and wellness. One of the most popular justifications for the use of VR over 2D is increased immersion and engagement. However, very little fundamental research has been produced evaluating the comparative impact of immersive VR on the user’s cognitive, physiological, and emotional state.Methods: A within-subject cross-over study design was used to directly compare VR and 2D screen delivery of different subject matter content. Both physiological and self-report data were collected for scenes containing calming nature environments, aggressive social confrontations, and neutral content.Results: Compared to 2D, the VR delivery resulted in a higher sense of presence, higher ratings of engagement, fun, and privacy. Confrontational scenes were rated as more tense whilst calming scenes were rated as more relaxing when presented in VR compared to 2D. Physiological data indicated that the scenes promoted overall states of arousal and relaxation in accordance with the scene subject matter (both VR and 2D). However, heart rate (HR) and galvanic skin response (GSR) were consistently higher throughout the VR delivery condition compared to 2D, including responses during scenes of neutral and calming subject matter.Discussion: This discrepancy between emotional and physiological responses for calming and neutral content in VR suggest an elevated arousal response driven by VR immersion that is independent of the emotional and physiological responses to the subject matter itself. These findings have important implications for those looking to develop and utilize VR technology as a training and educational tool as they provide insights into the impact of immersion on the user.

Event Abstract Back to Event Regulation of arousal and heart rate variability via biofeedback in severe traumatic brain injury. Jacqueline A. Rushby1*, Heather Francis1, Alana Fisher1 and Skye McDonald1 1 University of New South Wales, Psychology, Australia Background: Severe traumatic brain injury (TBI) is known to result in impaired emotion regulation and arousal. Heart rate variability (HRV) is an index of autonomic (dys)regulation, reflecting the balance between sympathetic and parasympathetic influences on the central nervous system. Biofeedback techniques have been found to regulate disrupted HRV and alleviate symptoms in disorders associated with impaired arousal. The present study aimed to examine, whether adults with severe TBI show: i) HRV dysregulation at rest; ii) improved HRV regulation via a biofeedback intervention; as well as how these relate to aspects of emotion regulation. Methods: Thirty adults with severe TBI (Mean posttraumatic amnesia = 49.10 days; Range: 2.00 - 180) and 30 age-, education-, and gender-matched controls participated. Participants completed self-report measures of mood, emotional and cognitive empathy, and trait alexithymia. Relative reports of emotion regulation were also provided for TBI participants. Participants’ HRV and skin conductance levels (SCL) were measured under two 5-minute conditions: i) ’free-breathing’ (resting baseline); ii) ‘biofeedback’ (paced at 6 breaths per minute). Results: Among TBI participants, emotional empathy levels (p = .026), cognitive empathy levels (p < 0.01), and ability to identify and describe one’s own feelings, were all significantly lower compared to controls (p < 0.01). On the autonomic measures, HRV and SCL, there were no differences between the TBI and Control groups, either at rest or during biofeedback (all p > 0.05). Biofeedback training lead to significant increases (improvement) on all HRV measures (p < 0.05) and SCL (p = 0.014) for both groups. Higher HRV during biofeedback, and higher SCLs at rest and during biofeedback, correlated negatively with trait alexithymia subscales in both the overall sample and separate groups (p < 0.05), but not empathy, nor relative reports of emotion regulation (p > 0.05). Conclusion: These results suggest that HRV shows promise as an index of impaired autonomic regulation in severe TBI. Biofeedback training may help normalise HRV in severe TBI; improved autonomic regulation may ameliorate impairments on emotional self-awareness. Keywords: Traumatic Brain Injury, Heart rate variability, social cognition, emotion, Arousal Conference: Australasian Society for Psychophysiology, Inc, Coffs Harbour, Australia, 26 Nov - 28 Nov, 2014. Presentation Type: Oral Presentation Topic: Psychophysiology Citation: Rushby JA, Francis H, Fisher A and McDonald S (2014). Regulation of arousal and heart rate variability via biofeedback in severe traumatic brain injury.. Conference Abstract: Australasian Society for Psychophysiology, Inc. doi: 10.3389/conf.fnhum.2014.216.00024 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 23 Oct 2014; Published Online: 02 Dec 2014. * Correspondence: Dr. Jacqueline A Rushby, University of New South Wales, Psychology, Sydney, Australia, j.rushby@unsw.edu.au Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Jacqueline A Rushby Heather Francis Alana Fisher Skye McDonald Google Jacqueline A Rushby Heather Francis Alana Fisher Skye McDonald Google Scholar Jacqueline A Rushby Heather Francis Alana Fisher Skye McDonald PubMed Jacqueline A Rushby Heather Francis Alana Fisher Skye McDonald Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

This research focuses on the application of BmT (Box Music Therapy) as a music therapy medium for treating neurological patients in Martha Friska Multatuli hospital, Medan. The aim of this research is to test the performance of BmT and its effectiveness in treating patients with neurological disorders. In this case, the nervous disorders experienced by the patient are mild/severe strokes, pinched nerves, and weak nerves. The BmT is an instrument equipped with four main components, namely the Galvanic Skin Response (GSR) sensor to detect stress levels, the MPX5050dp sensor to detect Beat Per Minute (bpm). Memory card as a storage medium for 16 instrumental music works and songs created by composer Prof. Junita Batubara, S.Sn., M.Sn and headphones as a medium for listening to music. Through the use of quasi-experimental methods, the results of this study show that out of 22 (twenty two) outpatients in the neurology unit at Matha Friska Hospital in Medan, the patients experienced a decrease in GSR and BPM numbers based on the results of the numbers that appeared on the BmT monitor screen before and after being given music therapy treatment. Furthermore, researchers also obtained information that the patient experienced a feeling of relaxation, which eased the pain in the patient's limbs both during the process and after music therapy.

Computer simulations used to study food purchasing behavior can be separated into low immersion virtual environments (LIVE), which use personal computers and standard monitors to display a scene, and high immersion virtual environments (HIVE) which use virtual reality technology such as head-mounted displays to display a scene. These methods may differ in their ability to create feelings of presence or cybersickness that would influence the usefulness of these approaches. In this present study, thirty-one adults experienced a virtual supermarket or fast-food restaurant using a LIVE system or a HIVE system. Feelings of presence and cybersickness were measured using questionnaires or physiological responses (heart rate and electrodermal activity). The participants were also asked to rate their ability to complete the set task. The results of this study indicate that participants reported a higher sense of presence in the HIVE scenes as compared to the LIVE scenes (p < 0.05). The participant's heart rate and electrodermal activity were significantly higher in the HIVE scene treatment when compared to the LIVE scene (p < 0.05). There was no difference in the participant's ability to complete tasks in the different scenes. In addition, feelings of cybersickness were not different between the HIVE and LIVE scenes.

Differing associations of depersonalization with physiological response during rest and breath-focused mindfulness in a trauma-exposed female population

Effects of hemoencephalographic biofeedback with virtual reality on selected aspects of attention in children with ADHD

Virtual reality (VR) is rapidly evolving from a gaming novelty to a powerful tool in fields ranging from therapy to training. As its applications expand, understanding how our bodies respond to VR environments becomes crucial. A recent study delved into the physiological effects of fixating on 3D bubbles within a virtual reality setting, revealing fascinating insights into the connections between visual attention, immersion, and our internal state. The study focused on exploring the correlates of physiological responses, specifically measuring heart rate variability (HRV) and electrodermal activity (EDA), while participants visually fixated on dynamically presented 3D bubbles in a VR environment. Why bubbles? Their dynamic, reflective, and semi-transparent nature makes them visually captivating and potentially evokes a sense of childlike wonder and intrigue. The result shows “Elevated Arousal” where fixations on the 3D-bubbles led to a noticeable increase in Skin conductance response (SCR), indicating heightened emotional arousal and engagement. This suggests that the visual stimulus was successfully capturing and maintaining the participant’s attention.

With the growing integration of automated driving (AD) functions in passenger vehicles, it is essential to focus not only on safety but also on passenger comfort which is often overlooked in the design process. Integrating this in the design cycle requires a thorough understanding of the relation between objective metrics and the subjective passenger response. This paper introduces a novel multimodal measurement platform for efficient measurement of objective metrics and subjective comfort in a representative AD setting. The platform, built on a commercially available electric vehicle, contains sensors to concurrently capture data on vehicle dynamics, environmental conditions, and passenger physiological responses. An automated data processing pipeline has been developed to compute and visualize metrics related to both vehicle performance and passenger comfort. The platform has been utilized in a proving ground jury test, with preliminary qualitative analyses identifying potential comfort-related indicators, such as Time-to-Collision and Galvanic Skin Response. The platform and its processing pipeline will be the basis for further investigation into objective-subjective comfort correlation and prediction in the future.

Attention problems, such as attention deficit hyperactivity disorder (ADHD), have a significant impact on quality of life and academic achievement. Using Electroencephalogram (EEG), Heart Rate Variability (HRV), and Galvanic Skin Response (GSR), this literature review analyzes the use of Artificial Intelligence (AI) in Neurofeedback Therapy (NFT). Although artificial intelligence has begun to be integrated into NFTs, its use is still in its early stages and presents a few challenges. The lack of representative datasets for training AI models, the complexities of processing biometric data (EEG, HRV, GSR), and the high infrastructure costs required for implementation are the key barriers to widespread adoption. Additionally, the general adoption of this technology is restricted by concerns regarding patient security and data privacy. The paper emphasizes the role of artificial intelligence in preprocessing data, feature extraction, and AI model analysis by integrating current research findings. The authors propose a novel approach to enhance the efficacy of neurofeedback therapy by integrating artificial intelligence with EEG, HRV, and GSR data, even though most of the content is derived from previous research. The researchers achieved substantial results by employing CNN, SVM, and LSTM to identify EEG, HRV, and GSR signals. However, challenges such as overfitting, data noise, and ethical concerns persist. The objective of this review is to offer a fair evaluation of the current state of artificial intelligence in NFT, identify gaps in the literature, and propose future research directions. These directions include the development of robust AI algorithms, the integration of NFT with emerging technologies such as virtual reality, ethical and societal implications, and AI-powered NFT applications. Additionally, this review proposes a framework for the integration of AI into NFT. AI-powered NFT has the potential to revolutionize the treatment of attention disorders and enhance the outcomes for those who are affected by surmounting these obstacles.