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Analysis of brain waves changes in stressful situations based on horror game with the implementation of virtual reality and braincomputer interface system: a case study

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Objectives: This presents a case for fear and stress stimuli and afterward EEG data analysis. Methods: The stress factor had been evoked by a computer horror game correlated with virtual reality (VR) and brain-computer interface (BCI) from OpenBCI, applied for the purpose of brain waves changes observation. Results: Results obtained during the initial study were promising and provide conclusions for further research in this field carried out on an expanded group of involved participants. Conclusions: The study provided very promising and interesting results. Further investigation with larger amount of participants will be carried out.
Rocznik
Strony
art. no. 20200050
Opis fizyczny
Bibliogr. 33 poz., rys., tab.
Twórcy
  • Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, Poland
  • Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, Poland
  • Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, Poland
  • Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, Poland
Bibliografia
  • 1. Padfield N, Zabalza J, Zhao H, Masero V, Ren J. EEG-based braincomputer interfaces using motor-imagery: techniques and challenges. Sensors 2019;19:1423.
  • 2. Mendoza LRM, Martinez MEM, Suarez AMS. The brain as a fundamental axis in learning process. Int Res J Eng IT Sci Res 2019; 5:38-45.
  • 3. Das S, Tripathy D, Raheja JL. An insight to the human brain and EEG. In Real-time BCI system design to control arduino based speed controllable robot using EEG Springer; 2019:13-24 pp.
  • 4. Zhang J. Secrets of the brain: an introduction to the brain anatomical structure and biological function. arXiv preprint arXiv: 190603314; 2019.
  • 5. Wierzgała P, Zapała D, Wojcik GM, Masiak J. Most popular signal processing methods in motor-imagery BCI: a review and metaanalysis. Front Neuroinf 2018;12:78.
  • 6. Wojcik GM, Masiak J, Kawiak A, Schneider P, Kwasniewicz L, Polak N. New protocol for quantitative analysis of brain cortex electroencephalographic activity in patients with psychiatric disorders. Front Neuroinf 2018;12:27.
  • 7. Rak RJ, Kołodziej M, Majkowski A. Brain-computer interface as measurement and control system the review paper. Metrol Meas Syst 2012;19:427-44.
  • 8. Wojcik GM, Masiak J, Kawiak A, Kwasniewicz L, Schneider P, Polak N, et al.. Mapping the human brain in frequency band analysis of brain cortex electroencephalographic activity for selected psychiatric disorders. Front Neuroinf 2018;12:73.
  • 9. Kołodziej M, Majkowski A, Oskwarek Ł, Rak RJ, Tarnowski P. Processing and Analysis of EEG Signal for SSVEP Detection. In Polish Conference on Biocybernetics and Biomedical Engineering. Springer; 2017:3-21 pp.
  • 10. Hong KS, Khan MJ, Hong MJ. Feature extraction and classification methods for hybrid f NIRS-EEG brain-computer interfaces. Front Hum Neurosci 2018;12:246.
  • 11. Kotyra S, Wojcik GM. Steady state visually evoked potentials and their analysis with graphical and acoustic transformation. In Polish conference on Biocybernetics and biomedical Engineering. Springer; 2017:22-31 pp.
  • 12. Kawala-Janik A, Pelc M, Podpora M. Method for EEG signals pattern recognition in embedded systems. Elektronika ir Elektrotechnika 2015;21:3-9.
  • 13. Samson V, Kitti BP, Kumar SP, Babu DS, Monica C. Electroencephalogram-based OpenBCI devices for disabled people. In Proceedings of 2nd International conference on MicroElectronics, Electromagnetics and Telecommunications. Springer; 2018:229-38 pp.
  • 14. Ewing KC, Fairclough SH, Gilleade K. Evaluation of an adaptive game that uses EEG measures validated during the design process as inputs to a biocybernetic loop. Front Hum Neurosci 2016;10:223.
  • 15. Jost TA, Nelson B, Rylander J. Quantitative analysis of the Oculus Rift S in controlled movement. Disabil Rehabil Assist Technol 2019;1-5. https://doi.org/10.1080/17483107.2019.1688398.
  • 16. Chabance M, Cattan G, Maureille B. Implementation of a daemon for OpenBCI. arXiv preprint arXiv:190404015; 2019.
  • 17. Craig J. Adaptive audio engine for EEG-based horror game. In Audio Engineering Society convention 142 Audio Engineering Society; 2017.
  • 18. Doumbouya R, Benlamine MS, Dufresne A, Frasson C. Game scenes evaluation and player’s dominant emotion prediction. In International conference on intelligent Tutoring systems. Springer; 2018:54-65 pp.
  • 19. Garner T. Identifying habitual statistical features of EEG in response to fear-related stimuli in an audio-only computer video game. In Proceedings of the 8th Audio mostly conference; 2013:1-6 pp.
  • 20. Vachiratamporn V, Moriyama K, Fukui K, Numao M. An implementation of affective adaptation in survival horror games. In 2014 IEEE Conference on Computational Intelligence and Games. IEEE; 2014:1-8 pp.
  • 21. Tóth V. Measurement of stress intensity using EEG. Computer science engineering B Sc thesis. Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics; 2015.
  • 22. Carofiglio V, De Carolis BN, D’Errico F. A BCI-based assessment of a player’s state of mind for game adaptation. In Proceedings of GHItaly19-3rd International Workshop on games-human interaction; 2019.
  • 23. Nogueira PA, Torres V, Rodrigues R, Oliveira E, Nacke LE. Vanishing scares: biofeedback modulation of affective player experiences in a procedural horror game. J Multimodal User Interfaces 2016;10:31-62.
  • 24. Fuentes-García JP, Pereira T, Castro MA, Carvalho Santos A, Villafaina S. Heart and brain responses to real versus simulated chess games in trained chess players: a quantitative EEG and HRV study. Int J Environ Res Publ Health 2019;16:5021.
  • 25. Wang Q, Sourina O, Nguyen MK. Eeg-based “serious” games design for medical applications. In 2010 International Conference on Cyberworlds. IEEE; 2010:270-6 pp.
  • 26. Suhaimi NS, Yuan CTB, Teo J, Mountstephens J. Modeling the affective space of 360 virtual reality videos based on arousal and valence for wearable EEG-based VR emotion classification. In 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA). IEEE; 2018:167-72 pp.
  • 27. OpenBCI. OpenBCI website; 2020. Available from: https://openbci.com.
  • 28. Roth D, Westermeier F, Brübach L, Feigl T, Schell C, Latoschik ME. Brain 2 communicate: EEG-based affect recognition to augment virtual social interactions. Mensch und Computer 2019-Workshopband; 2019.
  • 29. Lee TG. Data pattern modeling for bio-information processing based on OpenBCI platform. J Converg Cult Technol 2019;5:451-6.
  • 30. Hu PC, Chen PH, Kuo PC. Educational model based on hands-on brain-computer interface: implementation of music composition using EEG. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE; 2018:982-5 pp.
  • 31. Nicoll B, Keogh B. The Unity game engine and the circuits of cultural software. In: The Unity game engine and the circuits of cultural software. Springer; 2019:1-21 pp.
  • 32. Sulaiman N, Taib MN, Lias S, Murat ZH, Aris SA, Hamid NHA. Novel methods for stress features identification using EEG signals. Int J Sim: Syst Sci Technol 2011;12:27-33.
  • 33. Haak M, Bos S, Panic S, Rothkrantz L. Detecting stress using eye blinks and brain activity from EEG signals. In Proceeding of the 1st driver car interaction and interface. DCII 2008; 2009:35-60 pp.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8295c379-42e0-4211-a9ac-c84ae489bea1
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