Real Time EEG Based Measurements of Cognitive Load Indicates Mental States During Learning
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Published
Dec 23, 2017
Alex Dan
Miriam Reiner
Abstract
One of the recommended approaches in instructional design methods is to optimize the value of working memory capacity and avoid cognitive overload. Educational neuroscience offers innovative processes and methodologies to analyze cognitive load based on physiological measures. Observing psychophysiological changes when they occur in response to the course of a learning session allows adjustments in the learning session based on the individual learner's capabilities. The availability of non-invasive electroencephalogram (EEG)-based devices and advanced near-real-time analysis techniques have improved our understanding of the underlying mechanisms and have impacted the way we design instructional methods and adapt them to the current learner's cognitive load and valence states. We review Cognitive Load Theory, how cognitive load may be measured, and how analysis of EEG data can be applied to enhance learning through real-time measurements of the learner's cognitive load. We show an experiment that provides a proof of concept of real-time measures based on EEG indicators and of mental states during learning.
How to Cite
Dan, A., & Reiner, M. (2017). Real Time EEG Based Measurements of Cognitive Load Indicates Mental States During Learning. Journal of Educational Data Mining, 9(2), 31–44. https://doi.org/10.5281/zenodo.3554719
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Keywords
cognitive load, EEG, adaptive learning
References
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BORGHINI, G., ASTOLFI, L., VECCHIATO, G., MATTIA, D., AND BABILONI, F. 2012. Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neuroscience and Biobehavioral Reviews, 44, 58–75. http://doi.org/10.1016/j.neubiorev.2012.10.003
BROUWER, A.-M., HOGERVORST, M. A, VAN ERP, J. B. F., HEFFELAAR, T., ZIMMERMAN, P. H., AND OOSTENVELD, R. 2012. Estimating workload using EEG spectral power and ERPs in the n-back task. Journal of Neural Engineering, 9(4), 045008. http://doi.org/10.1088/1741-2560/9/4/045008
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CHIK, D. 2013. Theta-Alpha cross-frequency synchronization facilitates working memory control – a modeling study. SpringerPlus, 2(1), p.14. http://doi.org/10.1186/2193-1801-2- 14
COOPER, G. 2008. Research into cognitive load theory and instructional design at UNSW. Retrieved from http://dwb4.unl.edu/QQQ/SW/UNSW.htm 2/27/2008
DAS, R., CHATTERJEE, D., DAS, D., SINHARAY, A., & SINHA, A. 2014. Cognitive load measurement - A methodology to compare low cost commercial EEG devices. In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 1188–1194). IEEE. http://doi.org/10.1109/ICACCI.2014.6968528
DELEEUW, K. E., AND MAYER, R. E. 2008. A comparison of three measures of cognitive load: Evidence for separable measures of intrinsic, extraneous, and germane load. Journal of Educational Psychology, 100(1), 223–234. http://doi.org/10.1037/0022-0663.100.1.223
DEVONSHIRE, I. M., AND DOMMETT, E. J. 2010. Neuroscience: viable applications in education? The Neuroscientist: A Review Journal Bringing Neurobiology, Neurology and Psychiatry, 16(4), 349–56. http://doi.org/10.1177/1073858410370900
DIRICAN, A. C., AND GÖKTÜRK, M. 2011. Psychophysiological measures of human cognitive states applied in human computer interaction. Procedia Computer Science, 3, 1361–1367. http://doi.org/10.1016/j.procs.2011.01.016
EVERHART, D. E., AND DEMAREE, H. A. 2003. Low Alpha power (7.5-9.5 Hz) changes during positive and negative affective learning. Cognitive, Affective and Behavioral Neuroscience, 3(1), 39–45. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12822597
EWING, K. C., AND FAIRCLOUGH, S. H. 2010. The effect of an extrinsic incentive on psychophysiological measures of mental effort and motivational disposition when task demand is varied. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 54(3), 259–263. http://doi.org/10.1177/154193121005400316
EYSINK, T. H. S., AND DE JONG, T. 2012. Does instructional approach matter? How elaboration plays a crucial role in multimedia learning. Journal of the Learning Sciences, 21(4), 583–625. http://doi.org/10.1080/10508406.2011.611776
FINK, A, GRABNER, R. H., NEUPER, C., AND NEUBAUER, A. C. 2005. EEG Alpha band dissociation with increasing task demands. Cognitive Brain Research, 24(2), 252–259. http://doi.org/10.1016/j.cogbrainres.2005.02.002
GEVINS, A., SMITH, M. E., LEONG, H., MCEVOY, L., WHITFIELD, S., DU, R., and RUSH, G. 1998. Monitoring working memory load during computer-based tasks with EEG pattern recognition methods. Human Factors, 40(1), 79–91. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/9579105
GEORGEON, O. L., AND RITTER, F. E. 2012. An intrinsically-motivated schema mechanism to model and simulate emergent cognition. Cognitive Systems Research, 15- 16, 73–92. http://doi.org/10.1016/j.cogsys.2011.07.003
GERJETS, P., SCHEITER, K., AND CATRAMBONE, R. 2004. Designing instructional examples to reduce intrinsic cognitive load: molar versus modular presentation of solution procedures. Instructional Science, 32(1/2), 33–58. http://doi.org/10.1023/B:TRUC.0000021809.10236.71
GEVINS, A., SMITH, M. E., LEONG, H., MCEVOY, L., WHITFIELD, S., DU, R., and RUSH, G. 1998. Monitoring working memory load during computer-based tasks with EEG pattern recognition methods. Human Factors, 40(1), 79–91. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/9579105
GOLDMAN, S. R. 2009. Explorations of relationships among learners, tasks, and learning. Learning and Instruction, 19(5), 451–454. http://doi.org/10.1016/j.learninstruc.2009.02.006
GOSWAMI, U., AND SZŰCS, D. 2011. Educational neuroscience: developmental mechanisms: Towards a conceptual framework. NeuroImage, 57(3), 651–658. http://doi.org/10.1016/j.neuroimage.2010.08.072
GRIMES, D., TAN, D. S., HUDSON, S. E., SHENOY, P., AND RAO, R. P. N. 2008. Feasibility and pragmatics of classifying working memory load with an electroencephalograph. In Proceeding of the twenty-sixth annual CHI conference on Human factors in computing systems - CHI ’08 (p. 835). New York, New York, USA: ACM Press. http://doi.org/10.1145/1357054.1357187
HANCOCK, P. A., AND MESHKATI, N. 1988. Human mental workload. Amsterdam: North-Holland.
HÖFFLER, T. N., AND LEUTNER, D. 2007. Instructional animation versus static pictures: a meta-analysis. Learning and Instruction, 17(6), 722–738. http://doi.org/10.1016/j.learninstruc.2007.09.013
HOLM, A., LUKANDER, K., KORPELA, J., SALLINEN, M., AND MÜLLER, K. M. I. 2009. Estimating brain load from the EEG. The Scientific World Journal, 9, 639–51. http://doi.org/10.1100/tsw.2009.83
IMMORDINO-YANG, M. H., AND FISCHER, K. W. 2010. Neuroscience bases of learning. International encyclopedia of education, 3, 310-16.
KLIMESCH, W. 1999. EEG Alpha and Theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Research Reviews, 29(2-3), 169–195. http://doi.org/10.1016/S0165-0173(98)00056-3
KOHLMORGEN, J., DORNHEGE, G., BRAUN, M., BLANKERTZ, B., MÜLLER, K. R., CURIO, G., ... AND KINCSES, W. 2007. Improving human performance in a real operating environment through real-time mental workload detection. Toward BrainComputer Interfacing, 409-422.
LEI, S., AND ROETTING, M. 2011. Influence of task combination on EEG spectrum modulation for driver workload estimation. Human Factors: The Journal of the Human Factors and Ergonomics Society, 53(2), 168–179. http://doi.org/10.1177/0018720811400601
LEPPINK, J., PAAS, F., VAN DER VLEUTEN, C. P. M., VAN GOG, T., AND VAN MERRIËNBOER, J. J. G. 2013. Development of an instrument for measuring different types of cognitive load. Behavior Research Methods, 45(4), 1058–1072. http://doi.org/10.3758/s13428-013-0334-1
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