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Tytuł artykułu

EEG spectral analysis of human cognitive workload study

Identyfikatory
Warianty tytułu
PL
Badanie obciążenia poznawczego człowieka za pomocą analizy widmowej sygnału EEG
Języki publikacji
EN
Abstrakty
EN
The paper presents an experiment performed in order to confirm the hypothesis that the level of human cognitive workload can be studied by spectral anal-ysis. The experiment contained intervals ensuring high cognitive load. They were dis-played alternately with relaxing breaks. The spectral analysis covered changes in EEG bands including the alpha/theta ratio.
PL
Artykuł przedstawia wyniki eksperymentu, który został zrealizowa-ny w celu potwierdzenia hipotezy, że poziom obciążenia poznawczego można zbadać za pomocą analizy widmowej sygnału EEG. Eksperyment złożony był z interwałów zapewniających badanym wysokie obciążenie poznawcze (zadania matematyczne), wyświetlanych na przemian z interwałami relaksującymi. Analiza widmowa objęła fa-le EEG włączając w to stosunek alpha/theta.
Czasopismo
Rocznik
Strony
17--30
Opis fizyczny
Bibliogr. 28 poz.
Twórcy
  • Lublin University of Technology, Institute of Computer Science, ul. Nadbystrzycka 36b, 20-618 Lublin, Poland
autor
  • Lublin University of Technology, Institute of Computer Science, ul. Nadbystrzycka 36b, 20-618 Lublin, Poland
  • Lublin University of Technology, Institute of Computer Science, ul. Nadbystrzycka 36b, 20-618 Lublin, Poland
  • Lublin University of Technology, Institute of Computer Science, ul. Nadbystrzycka 36b, 20-618 Lublin, Poland
autor
  • Lublin University of Technology, Institute of Computer Science, ul. Nadbystrzycka 36b, 20-618 Lublin, Poland
autor
  • Lublin University of Technology, Institute of Computer Science, ul. Nadbystrzycka 36b, 20-618 Lublin, Poland
autor
  • Lublin University of Technology, Institute of Computer Science, ul. Nadbystrzycka 36b, 20-618 Lublin, Poland
Bibliografia
  • 1. Anderson E.W., Potter K.C., Matzen L.E., Shepherd J.F., Preston G.A., Silva C.T.: A User Study of Visualization Effectiveness Using EEG and Cognitive Load. [in:] Hauser H., Pfister H., van Wijk J.J. (eds): Eurographics, IEEE Symposium on Visuali-zation, Vol. 30, No. 3, 2011, p. 791÷800.
  • 2. Antonenko P., Paas F., Grabner R., Van Gog, T.: Using electroencephalography to measure cognitive load. Educational Psychology Review, Vol. 22(4), 2010, p. 425÷438.
  • 3. Bagyaraj S., Devi S.S.: Analysis of Spectral Features of EEG during four different Cognitive Tasks 1. International Journal of Engineering and Technology (IJET).
  • 4. Farwell L.A., Donchin E.: Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and clinical Neuro-physiology, Vol. 70(6), 1988, p. 510÷523.
  • 5. Gevins A., Smith M.E., McEvoy L., Yu D.: High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. Cerebral cortex, Vol. 7(4), 1997, p. 374÷385.
  • 6. Gevins A., Smith M.E., Leong H., McEvoy L., Whitfield S., Du R., Rush G.: Monitor-ing working memory load during computer-based tasks with EEG pattern recognition methods. Human Factors: The Journal of the Human Factors and Ergonomics Society, Vol. 40(1), 1998, p. 79÷91.
  • 7. Grimes D., Tan D., Hudson S., Shenoy P., Rao R.: Feasibility and pragmatics of classi-fying working memory load with an electroencephalograph. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2008, p. 835÷844.
  • 8. Gundel A., Wilson G.F.: Topographical changes in the ongoing EEG related to the dif-ficulty of mental tasks. Brain topography, Vol. 5(1), 1992, p. 17÷25.
  • 9. Klimesch W.: EEG alpha and theta oscillations reflect cognitive and memory perform-ance: a review and analysis. Brain research reviews, Vol. 29, No. 2, 1999, p.169÷195.
  • 10. Klimesch W., Schimke H., Pfurtscheller G.: Alpha Frequency, Cognitive Load and Memory Performance. Brain Topography, Vol. 5, No. 3, 1993, p. 241÷251.
  • 11. Kothe C.A., Makeig S.: Estimation of task workload from EEG data: new and current tools and perspectives. Engineering in Medicine and Biology Society, EMBC, Annual International Conference of the IEEE, 2011, p. 6547÷6551.
  • 12. Kumar N., Kumar J.: Measurement of Cognitive Load in HCI Systems Using EEG Power Spectrum: An Experimental Study. Procedia Computer Science, Vol. 84, 2016, p. 70÷78.
  • 13. Kumar N., Kumar J.: Measurement of efficiency of auditory vs visual communication in HMI: A cognitive load approach. International Conference on Advances in Human Machine Interaction (HMI), 2016, p. 1÷8.
  • 14. Mak J.N., Wolpaw J.R.: Clinical applications of brain-computer interfaces: current state and future prospects. Biomedical Engineering, Vol. 2, 2009, p. 187÷199.
  • 15. Meltzer J.A., Zaveri H.P., Goncharova I.I., Distasio M.M., Papademetris X., Spencer S.S., Spencer D., Constable, R.T.: Effects of working memory load on oscillatory power in human intracranial EEG. Cerebral Cortex, Vol. 18(8), 2008, p. 1843÷1855.
  • 16. Nourbakhsh N., Wang Y., Chen F.: GSR and blink features for cognitive load classifi-cation. In Human-Computer Interaction-INTERACT 2013, Springer Berlin Heidelberg 2013, p. 159÷166.
  • 17. Palaniappan R., Raveendran P.: Cognitive task prediction using parametric spectral analysis of EEG signals. Malaysian Journal of Computer Science, Vol. 14, No. 1, 2001, p. 58÷67.
  • 18. Pfurtscheller G., Neuper C.: Motor imagery and direct brain-computer communication. Proceedings of the IEEE, Vol. 89 (7), 2001, p. 1123÷1134.
  • 19. Plechawska-Wójcik M., Borys M.: An analysis of EEG signal combined with pupillary response in the dynamics of human cognitive processing. 2016 9th International Confer-ence on Human System Interactions (HSI), Portsmouth 2016, p. 378÷385.
  • 20. Rozado D., Dunser A.: Combining EEG with Pupillometry to Improve Cognitive Workload Detection. Computer, Vol. 48(10), 2015, p. 18÷25.
  • 21. Skublewska-Paszkowska M., Łukasik E., Smolka J., Miłosz M., Plechawska-Wójcik M., Borys M., Dzienkowski M.: Comprehensive measurements of human motion pa-rameters in research projects. Proceedings of the 10th International Technology, Educa-tion and Development Conference (INTED 2016), 2016, p. 8597÷8605.
  • 22. Zarjam P., Epps J., Chen F.: Characterizing working memory load using EEG delta ac-tivity. 19th European Signal Processing Conference, 2011, p. 1554÷1558.
  • 23. Zarjam P., Epps J., Chen F., Lovell N.H.: Classification of working memory load using wavelet complexity features of EEG signals. International Conference on Neural Infor-mation Processing, Springer, Berlin Heidelberg 2012, p. 692÷699.
  • 24. Zarjam P., Epps J., Chen F.: Spectral EEG featuresfor evaluating cognitive load. Engi-neering in Medicine and Biology Society, EMBC, Annual International Conference of the IEEE, EMBS Boston 2011, p. 3841÷3844.
  • 25. Kim J.H., Kim D.W., Im C.H.: Brain Areas Responsible for Vigilance: An EEG Source Imaging Study. Brain Topography, 2017, p. 1÷9.
  • 26. Cunnington R., Iansek R., Bradshaw J.L., Phillips J.G.: Movement-related potentials associated with movement preparation and motor imagery. Experimental brain research, Vol. 111(3), 1996, p. 429÷436.
  • 27. Beverina F., Palmas G., Silvoni S., Piccione F., Giove S.: User adaptive BCIs: SSVEP and P300 based interfaces. PsychNol. J., 2003.
  • 28. Tong S., Thakor N.V.: Quantitative EEG analysis methods and clinical applications. Artech House, 2009.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-053feacd-1a22-43c8-b229-21bdb49c4e7c
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