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De Francesco L, Mazza A, Sorrenti M, Murino V, Battegazzorre E, Strada F, Bottino AG, Dal Monte O. Cooperation and competition have same benefits but different costs. iScience 2024; 27:110292. [PMID: 39045102 PMCID: PMC11263633 DOI: 10.1016/j.isci.2024.110292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 05/27/2024] [Accepted: 06/14/2024] [Indexed: 07/25/2024] Open
Abstract
Cooperation and competition shape everyday human interactions and impact individuals' chances of success in different domains. Using a virtual Stroop test, classically employed to assess general cognitive interference, we examined the impact of social context (cooperation and competition) and other's ability (higher and lower performers) on performance, perceived stress, and autonomic activity. In Experiment 1, we found that both cooperation with a lower performer and competition with a higher performer led to similar enhancement in performance. However, only competition with a more skilled opponent induced an increase in perceived stress and physiological activity. Experiment 2 further demonstrated that these effects persisted even with prolonged exposure to these contexts. In summary, cooperation can be just as effective as competition in improving individuals' performance. However, cooperation does not carry the same level of stress and physiological burden as the competitive context, representing a healthier and more optimal way to boost individual performance.
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Affiliation(s)
| | | | | | - Virginia Murino
- Department of Psychology, University of Turin, Torino, Italy
| | | | - Francesco Strada
- Department of Control and Computer Engineering, Politecnico di Torino, Italy
| | - Andrea G. Bottino
- Department of Control and Computer Engineering, Politecnico di Torino, Italy
| | - Olga Dal Monte
- Department of Psychology, University of Turin, Torino, Italy
- Department of Psychology, Yale University, New Haven, CT, USA
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Moreno-Fernández RD, Bernabéu-Brotons E, Carbonell-Colomer M, Buades-Sitjar F, Sampedro-Piquero P. Sex-related differences in young binge drinkers on the neurophysiological response to stress in virtual reality. Front Public Health 2024; 12:1348960. [PMID: 38947350 PMCID: PMC11211283 DOI: 10.3389/fpubh.2024.1348960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 06/06/2024] [Indexed: 07/02/2024] Open
Abstract
Background Stress is one of the main environmental factors involved in the onset of different psychopathologies. In youth, stressful life events can trigger inappropriate and health-damaging behaviors, such as binge drinking. This behavior, in turn, can lead to long-lasting changes in the neurophysiological response to stress and the development of psychological disorders late in life, e.g., alcohol use disorder. Our aim was to analyze the pattern of neurophysiological responses triggered with the exposition to a stressful virtual environment in young binge drinkers. Methods AUDIT-3 (third question from the full AUDIT) was used to detect binge drinking (BD) in our young sample (age 18-25 years). According to the score, participants were divided into control (CO) and BD group. Next, a standardized virtual reality (VR) scenario (Richie's Plank) was used for triggering the stress response while measuring the following neurophysiological variables: brain electrical activity by electroencephalogram (EEG) and cortisol levels through saliva samples both measurements registered before and after the stressful situation. Besides, heart rate (HR) with a pulsometer and electrodermal response (EDA) through electrodes placed on fingers were analyzed before, during and after the VR task. Results Regarding the behavior assessed during the VR task, BD group spent significantly less amount of time walking forward the table and a tendency toward more time walking backwards. There was no statistically significant difference between the BD and the CO group regarding time looking down, but when we controlled the variable sex, the BD women group displayed higher amount of time looking down than the rest of the groups. Neurophysiological measurements revealed that there was not any statistically significant difference between groups in any of the EEG registered measures, EDA response and cortisol levels. Sex-related differences were found in HR response to VR scenario, in which BD women displayed the highest peak of response to the stressor. Also, the change in heartbeat was higher in BD women than men. Conclusion Unveiling the neurophysiological alterations associated with BD can help us to prevent and detect early onset of alcohol use disorder. Also, from our data we conclude that participants' sex can modulate some stress responses, especially when unhealthy behaviors such as BD are present. Nevertheless, the moment of registration of the neurophysiological variables respect to the stressor seems to be a crucial variable.
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Affiliation(s)
| | | | | | - Francisco Buades-Sitjar
- Departamento de Psicología Biológica y de la Salud, Facultad de Psicología, Universidad Autónoma de Madrid, Madrid, Spain
| | - Patricia Sampedro-Piquero
- Departamento de Psicología Biológica y de la Salud, Facultad de Psicología, Universidad Autónoma de Madrid, Madrid, Spain
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Peng K, Moussavi Z, Karunakaran KD, Borsook D, Lesage F, Nguyen DK. iVR-fNIRS: studying brain functions in a fully immersive virtual environment. NEUROPHOTONICS 2024; 11:020601. [PMID: 38577629 PMCID: PMC10993907 DOI: 10.1117/1.nph.11.2.020601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 03/05/2024] [Accepted: 03/06/2024] [Indexed: 04/06/2024]
Abstract
Immersive virtual reality (iVR) employs head-mounted displays or cave-like environments to create a sensory-rich virtual experience that simulates the physical presence of a user in a digital space. The technology holds immense promise in neuroscience research and therapy. In particular, virtual reality (VR) technologies facilitate the development of diverse tasks and scenarios closely mirroring real-life situations to stimulate the brain within a controlled and secure setting. It also offers a cost-effective solution in providing a similar sense of interaction to users when conventional stimulation methods are limited or unfeasible. Although combining iVR with traditional brain imaging techniques may be difficult due to signal interference or instrumental issues, recent work has proposed the use of functional near infrared spectroscopy (fNIRS) in conjunction with iVR for versatile brain stimulation paradigms and flexible examination of brain responses. We present a comprehensive review of current research studies employing an iVR-fNIRS setup, covering device types, stimulation approaches, data analysis methods, and major scientific findings. The literature demonstrates a high potential for iVR-fNIRS to explore various types of cognitive, behavioral, and motor functions in a fully immersive VR (iVR) environment. Such studies should set a foundation for adaptive iVR programs for both training (e.g., in novel environments) and clinical therapeutics (e.g., pain, motor and sensory disorders and other psychiatric conditions).
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Affiliation(s)
- Ke Peng
- University of Manitoba, Department of Electrical and Computer Engineering, Price Faculty of Engineering, Winnipeg, Manitoba, Canada
| | - Zahra Moussavi
- University of Manitoba, Department of Electrical and Computer Engineering, Price Faculty of Engineering, Winnipeg, Manitoba, Canada
| | - Keerthana Deepti Karunakaran
- Massachusetts General Hospital, Harvard Medical School, Department of Psychiatry, Boston, Massachusetts, United States
| | - David Borsook
- Massachusetts General Hospital, Harvard Medical School, Department of Psychiatry, Boston, Massachusetts, United States
- Massachusetts General Hospital, Harvard Medical School, Department of Radiology, Boston, Massachusetts, United States
| | - Frédéric Lesage
- University of Montreal, Institute of Biomedical Engineering, Department of Electrical Engineering, Ecole Polytechnique, Montreal, Quebec, Canada
- Montreal Heart Institute, Montreal, Quebec, Canada
| | - Dang Khoa Nguyen
- University of Montreal, Department of Neurosciences, Montreal, Quebec, Canada
- Research Center of the Hospital Center of the University of Montreal, Department of Neurology, Montreal, Quebec, Canada
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Hag A, Al-Shargie F, Handayani D, Asadi H. Mental Stress Classification Based on Selected Electroencephalography Channels Using Correlation Coefficient of Hjorth Parameters. Brain Sci 2023; 13:1340. [PMID: 37759941 PMCID: PMC10527440 DOI: 10.3390/brainsci13091340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/11/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
Electroencephalography (EEG) signals offer invaluable insights into diverse activities of the human brain, including the intricate physiological and psychological responses associated with mental stress. A major challenge, however, is accurately identifying mental stress while mitigating the limitations associated with a large number of EEG channels. Such limitations encompass computational complexity, potential overfitting, and the prolonged setup time for electrode placement, all of which can hinder practical applications. To address these challenges, this study presents the novel CCHP method, aimed at identifying and ranking commonly optimal EEG channels based on their sensitivity to the mental stress state. This method's uniqueness lies in its ability not only to find common channels, but also to prioritize them according to their responsiveness to stress, ensuring consistency across subjects and making it potentially transformative for real-world applications. From our rigorous examinations, eight channels emerged as universally optimal in detecting stress variances across participants. Leveraging features from the time, frequency, and time-frequency domains of these channels, and employing machine learning algorithms, notably RLDA, SVM, and KNN, our approach achieved a remarkable accuracy of 81.56% with the SVM algorithm outperforming existing methodologies. The implications of this research are profound, offering a stepping stone toward the development of real-time stress detection devices, and consequently, enabling clinicians to make more informed therapeutic decisions based on comprehensive brain activity monitoring.
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Affiliation(s)
- Ala Hag
- School of Computer Science & Engineering, Taylor’s University, Jalan Taylors, Subang Jaya 47500, Selangor, Malaysia;
| | - Fares Al-Shargie
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, Australia
| | - Dini Handayani
- Department of Electrical Engineering, Abu Dhabi University, Abu Dhabi P.O. Box 59911, United Arab Emirates;
| | - Houshyar Asadi
- Computer Science Department, KICT, International Islamic University Malaysia, Kuala Lumpur 53100, Selangor, Malaysia
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Khajuria A, Kumar A, Joshi D, Kumaran SS. Reducing Stress with Yoga: A Systematic Review Based on Multimodal Biosignals. Int J Yoga 2023; 16:156-170. [PMID: 38463652 PMCID: PMC10919405 DOI: 10.4103/ijoy.ijoy_218_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 12/20/2023] [Accepted: 12/22/2023] [Indexed: 03/12/2024] Open
Abstract
Stress is an enormous concern in our culture because it is the root cause of many health issues. Yoga asanas and mindfulness-based practices are becoming increasingly popular for stress management; nevertheless, the biological effect of these practices on stress reactivity is still a research domain. The purpose of this review is to emphasize various biosignals that reflect stress reduction through various yoga-based practices. A comprehensive synthesis of numerous prior investigations in the existing literature was conducted. These investigations undertook a thorough examination of numerous biosignals. Various features are extracted from these signals, which are further explored to reflect the effectiveness of yoga practice in stress reduction. The multifaceted character of stress and the extensive research undertaken in this field indicate that the proposed approach would rely on multiple modalities. The notable growth of the body of literature pertaining to prospective yoga processes is deserving of attention; nonetheless, there exists a scarcity of research undertaken on these mechanisms. Hence, it is recommended that future studies adopt more stringent yoga methods and ensure the incorporation of suitable participant cohorts.
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Affiliation(s)
- Aayushi Khajuria
- Department of NMR and MRI Facility, All India Institute of Medical Sciences, New Delhi, India
| | - Amit Kumar
- Center for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Deepak Joshi
- Center for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - S. Senthil Kumaran
- Department of NMR and MRI Facility, All India Institute of Medical Sciences, New Delhi, India
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Al-Shargie F, Badr Y, Tariq U, Babiloni F, Al-Mughairbi F, Al-Nashash H. Classification of Mental Stress Levels using EEG Connectivity and Convolutional Neural Networks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38083224 DOI: 10.1109/embc40787.2023.10340398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Classifying mental stress is important as it helps in identifying the type and severity of stress, which can inform the most appropriate treatment or intervention. In this study, we propose utilizing electroencephalography (EEG) signals with convolutional neural networks (CNNs) to classify four mental states: rest, control-alert, stress and stress mitigation. The mental stress state was induced using Stroop color word test (SCWT) with time constrains and was then mitigated using 16 Hz Binaural beat stimulation (BBs). We quantified the four mental states using the reaction time (RT) to stimuli, accuracy of target detection, subjective score, and functional connectivity images of EEG estimated by Phase Locking Value (PLV). Our results show that, the SCWT reduced the accuracy of target detection by 70% with (F= 24.56, p = .00001), and the BBs improved the accuracy by 28% (F= 4.54, p = .00470). The functional connectivity network showed different patterns between the frontal/occipital and parietal regions, under the four mental states. The proposed CNNs with PLV images differentiated between the four mental states with highest classification performance at beta frequency band with 80.95% accuracy, 80.36% sensitivity, 94.75% specificity, 83.63% precision and 81.96% F-score. The overall results suggest that 16 Hz BBs can be used as an effective method to mitigate stress and the proposed CNNs with EEG-PLV images as a promising method for classifying different mental states.
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Kim H, Song J, Kim S, Lee S, Park Y, Lee S, Lee S, Kim J. Recent Advances in Multiplexed Wearable Sensor Platforms for Real-Time Monitoring Lifetime Stress: A Review. BIOSENSORS 2023; 13:bios13040470. [PMID: 37185545 PMCID: PMC10136450 DOI: 10.3390/bios13040470] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/06/2023] [Accepted: 04/09/2023] [Indexed: 05/17/2023]
Abstract
Researchers are interested in measuring mental stress because it is linked to a variety of diseases. Real-time stress monitoring via wearable sensor systems can aid in the prevention of stress-related diseases by allowing stressors to be controlled immediately. Physical tests, such as heart rate or skin conductance, have recently been used to assess stress; however, these methods are easily influenced by daily life activities. As a result, for more accurate stress monitoring, validations requiring two or more stress-related biomarkers are demanded. In this review, the combinations of various types of sensors (hereafter referred to as multiplexed sensor systems) that can be applied to monitor stress are discussed, referring to physical and chemical biomarkers. Multiplexed sensor systems are classified as multiplexed physical sensors, multiplexed physical-chemical sensors, and multiplexed chemical sensors, with the effect of measuring multiple biomarkers and the ability to measure stress being the most important. The working principles of multiplexed sensor systems are subdivided, with advantages in measuring multiple biomarkers. Furthermore, stress-related chemical biomarkers are still limited to cortisol; however, we believe that by developing multiplexed sensor systems, it will be possible to explore new stress-related chemical biomarkers by confirming their correlations to cortisol. As a result, the potential for further development of multiplexed sensor systems, such as the development of wearable electronics for mental health management, is highlighted in this review.
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Affiliation(s)
- Heena Kim
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Jaeyoon Song
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Sehyeon Kim
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Suyoung Lee
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Yejin Park
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Seungjun Lee
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Seunghee Lee
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Jinsik Kim
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
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Christou V, Miltiadous A, Tsoulos I, Karvounis E, Tzimourta KD, Tsipouras MG, Anastasopoulos N, Tzallas AT, Giannakeas N. Evaluating the Window Size's Role in Automatic EEG Epilepsy Detection. SENSORS (BASEL, SWITZERLAND) 2022; 22:9233. [PMID: 36501935 PMCID: PMC9739775 DOI: 10.3390/s22239233] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/16/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
Electroencephalography is one of the most commonly used methods for extracting information about the brain's condition and can be used for diagnosing epilepsy. The EEG signal's wave shape contains vital information about the brain's state, which can be challenging to analyse and interpret by a human observer. Moreover, the characteristic waveforms of epilepsy (sharp waves, spikes) can occur randomly through time. Considering all the above reasons, automatic EEG signal extraction and analysis using computers can significantly impact the successful diagnosis of epilepsy. This research explores the impact of different window sizes on EEG signals' classification accuracy using four machine learning classifiers. The machine learning methods included a neural network with ten hidden nodes trained using three different training algorithms and the k-nearest neighbours classifier. The neural network training methods included the Broyden-Fletcher-Goldfarb-Shanno algorithm, the multistart method for global optimization problems, and a genetic algorithm. The current research utilized the University of Bonn dataset containing EEG data, divided into epochs having 50% overlap and window lengths ranging from 1 to 24 s. Then, statistical and spectral features were extracted and used to train the above four classifiers. The outcome from the above experiments showed that large window sizes with a length of about 21 s could positively impact the classification accuracy between the compared methods.
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Affiliation(s)
- Vasileios Christou
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece
| | - Andreas Miltiadous
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece
| | - Ioannis Tsoulos
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece
| | - Evaggelos Karvounis
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece
| | - Katerina D. Tzimourta
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece
- Department of Electrical and Computer Engineering, Faculty of Engineering, University of Western Macedonia, 50100 Kozani, Greece
| | - Markos G. Tsipouras
- Department of Electrical and Computer Engineering, Faculty of Engineering, University of Western Macedonia, 50100 Kozani, Greece
| | | | - Alexandros T. Tzallas
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece
| | - Nikolaos Giannakeas
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece
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