Assessment of mental fatigue and stress on electronic sport players with data fusion (original) (raw)
References
Cacioppo JT, Tassinary LG, Berntson GG (2007) Handbook of psychophysiology. Cambridge University Press, New York 914 p Google Scholar
Wan B, Wang Q, Su K, Dong C, Song W, Pang M (2021) Measuring the impacts of virtual reality games on cognitive ability using EEG signals and game performance data. IEEE Access 9:18326–18344 Article Google Scholar
Baddeley AD, Logie RH (1999) Working memory: the multiple component model. In: Miyake A, Shah P (eds) Models of working memory: mechanisms of active maintenance and executive control. Cambridge Univ. Press, Cambridge, pp 28–61 Chapter Google Scholar
Xiong R, Kong F, Yang X, Liu G, Wen W (2020) Pattern recognition of cognitive load using EEG and ECG signals. Sensors 20:5122 ArticlePubMed Central Google Scholar
Anderson CW, Bratman JA (2008) Translating thoughts into actions by finding patterns in brainwaves, in Proc. 14th Yale Workshop Adapt. Learn. Syst., 2008, pp. 1-6
Mohamed Z, El Halaby M, Said T, Shawky D, Badawi A (2018) Characterizing Focused Attention and Working Memory Using EEG. Sensors (Basel) 18(11):3743 Article Google Scholar
Antonenko PD, Niederhauser DS (2010) The influence of leads on cognitive load and learning in a hypertext environment. Comput Hum Behav 26(2):140–150 Article Google Scholar
Scharinger C, Kammerer Y, Gerjets P (2015) Pupil dilation and EEG alpha frequency band power reveal load on executive functions for link-selection processes during text reading. PLoS One 10(6):e0130608 ArticlePubMedPubMed CentralCAS Google Scholar
Khader PH, Jost K, Ranganath C, Rösler F (2010) Theta and alpha oscillations during working-memory maintenance predict successful long-term memory encoding. Neurosci Lett 468(3):339–343 ArticlePubMedCAS Google Scholar
Klimesch W (1999) EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Research. Brain Res Rev 29(2–3):169–195 ArticlePubMedCAS Google Scholar
Castro-Meneses LJ, Kruger JL, Doherty S (2020) Validating theta power as an objective measure of cognitive load in educational video. Education Tech Research Dev 68:181–202 Article Google Scholar
Gündoğdu S, Doğan EA, Gülbetekin E, Çolak ÖH, Polat Ö (2019) Evaluation of the EEG signals and eye tracker data for working different N-back modes. Traitement du Signal 36(6):493–500 Article Google Scholar
Sharma N, Gedeon T (2012) Objective measures, sensors and computational techniques for stress recognition and classification: a survey. Comput Methods Prog Biomed 108(3):1287–1301 Article Google Scholar
Lin T, John L (2006) Quantifying mental relaxation with EEG for use in computer games. International Conference on Internet Computing, pp. 409–415, Las Vegas, Nevada, USA
Pelegrina S, Lechuga MT, Madruga JAG, Elosúa MR, Macizo P, Carreiras M, Fuentes LJ, Bajo MT (2015) Normative data on the n-back task for children and young adolescents. Front Psychol 6(1544):1–11 Google Scholar
Wilhelm O, Hildebrandt A, Oberauer K (2013) What is working memory capacity, and how can we measure it? Front Psychol 433(4):1–22 Google Scholar
Tanaka M, Shigihara Y, Ishii A, Funakura M, Kanai E, Watanabe Y (2012) Effect of mental fatigue on the central nervous system: An electroencephalography study. Behav Brain Funct 8(48):1–8 Google Scholar
Tanaka M, Ishii A, Watanabe Y (2015) Effects of mental fatigue on brain activity and cognitive performance: A magnetoencephalography study. Anat Physiol 5(S4):1–5 CAS Google Scholar
Bates ME, Lemay E (2004) The d2 test of attention: construct validity and extensions in scoring techniques. J Int Neuropsychol Soc 10(3):392–400 ArticlePubMed Google Scholar
Tarnowski P, Kołodzıej M, Majkowski A, Rak R (2016) A system for synchronous acquisition of selected physiological signals aimed at emotion recognition. Przegląd Elektrotechniczny 92(12):327–331 Google Scholar
Gündoğdu S, Doğan EA, Gülbetekin E, Çolak ÖH, Polat Ö (2019) Bulmaca Video Oyunu Oynama Süresinin Stres ve Odaklanma Üzerindeki Etkilerinin Galvanik Deri Tepkisi, KHD ve Göz Takip Tabanlı Değerlendirilmesi. 4th International Mediterranean Scıence And Engıneerıng Congress. April 25-27, Alanya / TURKEY.
Rudolf K, Bickmann P, Froböse I, Tholl C, Wechsler K, Grieben C (2020) Demographics and Health Behavior of Video Game and eSports Players in Germany: The eSports Study 2019. Int J Environ Res Public Health 17(6):1870 ArticlePubMed Central Google Scholar
Wewers ME, Lowe NK (1990) A critical review of visual analogue scales in the measurement of clinical phenomena. Res Nurs Health 13:227–236 ArticlePubMedCAS Google Scholar
May T, Pridmore S (2020) A visual analogue scale companion for the six-item Hamilton Depression Rating Scale. Aust Psychol 55:3–9 Article Google Scholar
Lesage FX, Berjot S, Deschamps F (2012) Clinical stress assessment using a visual analogue scale. Occup Med 62(8):600–605 Article Google Scholar
Lee KA, Hicks G, Nino-Murcia G (1991) Validity and reliability of a scale to assess fatigue. Psychiatry Res 36(3):291–298 ArticlePubMedCAS Google Scholar
Matthews G, Desmond PA (2002) Task-induced fatigue states and simulated driving performance. Q J Exp Psychol A 55(2):659–686 ArticlePubMed Google Scholar
Zhang R (2019) The Effect of Meditation on Concentration Level and Cognitive Performance. Global J Health Sci 11(1):134–140 Article Google Scholar
Bird JJ, Faria DR, Manso LJ, Ekárt A, Buckingham CD (2019) A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction. Complexity:1–14. https://doi.org/10.1155/2019/4316548
Garcia-Moreno FM, Bermudez-Edo M, Garrido JL, Rodríguez-Fórtiz MJ (2020) Reducing Response Time in Motor Imagery Using A Headband and Deep Learning. Sensors 20:6730 ArticlePubMed Central Google Scholar
Wiechert G et al (2016) Identifying users and activities with cognitive signal processing from a wearable headband," 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Palo Alto, CA, USA, 2016, pp. 129-136
Zhao D, MacDonald S, Gaudi T, Uribe-Quevedo A, Martin MV, Kapralos B (2018) Facial expression detection employing a brain computer interface. In Proceedings of the 9th International Conference on Information, Intelligence, Systems and Applications (IISA), Zakynthos, Greece, 23–25 July; pp. 1–2
Salehzadeh A, Calitz AP, Greyling J (2020) Human activity recognition using deep electroencephalography learning. Biomed Signal Process Control 62:102094 Article Google Scholar
Alrige M, Chatterjee S (2015) Toward a taxonomy of wearable technologies in healthcare. In: New horizons in design science: Broadening the research agenda. Springer, Cham, pp 496–504 Chapter Google Scholar
Abujelala M, Abellanoza C, Sharma A, Makedon F (2016) Brain-EE: Brain enjoyment evaluation using commercial EEG headband. In: Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments. ACM, New York, pp 33:1–33:5 Google Scholar
Przegalinska A, Ciechanowski L, Magnuski M, Gloor P (2018) Muse Headband: Measuring Tool or a Collaborative Gadget? In: Grippa F, Leitão J, Gluesing J, Riopelle K, Gloor P (eds) Collaborative Innovation Networks. Studies on Entrepreneurship, Structural Change and Industrial Dynamics. Springer, Cham Google Scholar
Kasperiuniene J, Jariwala M, Vaskevicius E, Satkauskas S (2016) Affective engagement to virtual and live lectures. In: Dregvaite G, Damasevicius R (eds) Information and software technologies. Springer, Cham, pp 499–508 Chapter Google Scholar
Arnsten AF (2009) Stress signalling pathways that impair prefrontal cortex structure and function. Nature reviews. Neuroscience 10(6):410–422 PubMedCAS Google Scholar
Castillo O, Sotomayor S, Kemper G, Clement V (2021) Correspondence Between TOVA Test Results and Characteristics of EEG Signals Acquired Through the Muse Sensor in Positions AF7–AF8. In: Iano Y, Arthur R, Saotome O, Kemper G, Borges Monteiro AC (eds) Proceedings of the 5th Brazilian Technology Symposium. Smart Innovation, Systems and Technologies, vol 202. Springer, Cham Google Scholar
Mesquita RNO, Kyröläinen H, Olstad DS (2017) Reliability and validity of time domain heart rate variability during daily routine activities – an alternative to the morning orthostatic test? Biomed Hum Kinetics 9(1):64–68 Article Google Scholar
Kim PW, Lee S (2017) Audience real-time bio-signal-processing-based computational intelligence model for narrative scene editing. Multimed Tools Appl 76(23):24833–24845 Article Google Scholar
Zhang Xizheng Z, Ling Y, Weixiong W (2010) Wavelet Time-frequency Analysis of Electro-encephalogram (EEG) Processing. (IJACSA). Int J Adv Comput Sci Appl 1(5):1–5 Google Scholar
Olkkonen JT (2011) Discrete Wavelet Transforms - Theory and Applications
Orosco L, Correa AG, Laciar E (2013) Review: a survey of performance and techniques for automatic epilepsy detection. J Med Biol Eng 33(6):526–537 Article Google Scholar
Geethanjali B, Adalarasu K, Mohan J, Seshadri NPG (2018) Music induced brain functional connectivity using eeg sensors: a study on Indian Music. IEEE Sensors J 19(4):1–9 Google Scholar
Gall S et al (2017) Associations between selective attention and soil-transmitted helminth infections, socioeconomic status, and physical fitness in disadvantaged children in Port Elizabeth, South Africa: an observational study. PLoS Negl Trop Dis 11(5):1–19 Article Google Scholar
Sethi JK, Nagendra HR, Ganpat TS (2013) Yoga improves attention and self-esteem in underprivileged girl student. J Educ Health Promot 2(55):1–4 Google Scholar
Craig A, Tran Y, Wijesuriya N, Nguyenb H (2012) Regional brain wave activity changes associated with fatigue. Psychophysiology 49(4):574–582 ArticlePubMed Google Scholar
Gharagozlou F, Saraji GN, Mazloumi A, Nahvi A, Nasrabadi AM, Foroushani AR, Kheradmand AA, Ashouri M, Samavati M (2015) Detecting driver mental fatigue based on EEG alpha power changes during simulated driving. Iran J Public Health 44(12):1693–1700 PubMedPubMed Central Google Scholar
Yazgı S, ve Yıldız M (2009) Yutkunmanın kalp hızı değişkenliği üzerine etkisinin yok edilmesi, VI. Ulusal Tıp Bilişimi Kongresi, ss. 276-283, 12-15 Kasım, Akdeniz Üniversitesi, Antalya
Ateş O, Keskin B, ve Çotuk, H.B. (2017) İş yerinde zihinsel yüklenme ve egzersizin kalp hızı değişkenliği üzerindeki etkisi. Ulusal Spor Bilimleri Dergisi 1(2):55–65 Article Google Scholar
Acharya UR, Kannathal N, Sing OW, Ping LY, Chua T (2004) Heart rate analysis in normal subjects of various age groups. Biomed Eng Online 36(7):1140–1148 Google Scholar
Toledo E, Gurevitz O, Hod H, Eldar M, Akselrod S (2002) Thrombolysis in the eyes of the continuous wavelet transform. Comput Cardiol 29:657–660 Article Google Scholar
Bersak D, McDarby G, Augenblick N, McDarby P, McDonnell D, McDonald B, Karkun R (2001) Intelligent biofeedback using an immersive competitive environment. Designing Ubiquitous Computing Games Workshop at UbiComp, 30 September - 2 October, Atlanta, GA, USA
Lin T, Omato M, Hu W, Imamiya A (2005) Do physiological data relate to traditional usability indexes? Proceedings of the 17th Australia Conference on Computer–Human Interaction: Citizens Online: Considerations for Today and the Future, pp. 1–10, 21-25 November, Canberra, Australia
Shi Y, Ruiz N, Taib R, Choi EHC, Chen F (2007) Galvanic skin response (GSR) as an index of cognitive load. Extended Abstracts Proceedings of the 2007 Conference on Human Factors in Computing Systems, CHI’07, pp. 2651–2656, 28 April-3 May, San Jose, California, USA
Finger B, Murphy RO (2011) Using skin conductance in judgment and decision making research. In: Schulte-Mecklenbeck M, Kuehberger A, Ranyard R (eds) A handbook of process tracing methods for decision research. Psychology Press, New York, pp 163–184 Google Scholar
Utkutuğ, Ç.P. ve Alkibay, S. (2013) Nöropazarlama: reklam etkinliğinin psikofizyolojik tekniklerle değerlendirilmesi üzerine yapılmış araştırmalarının gözden geçirilmesi. H.Ü. İktisadi İdari Bilimler Fakültesi Dergisi 31(2):167–195 Google Scholar
Klebba JM (1985) Physiological measures of research: a review of brain activity, electrodermal response, pupil dilation, and voice analysis methods and studies. J Curr Issues Res Advert 8(1):53–76 Google Scholar
Gökay R, Masazade E, Aydin Ç, Barkana DE (2015) Emotional state and cognitive load analysis using features from bvp and sc sensors. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 178-183, 14-16 Sept, San Diego, CA, USA