EEG Based Analysis of Cognitive Load Enhance Instructional Analysis



Published Dec 23, 2017
Alex Dan Miriam Reiner


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 novel 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 will 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 a learning experiment in an attempt to provide a proof of concept of learning and real-time measures of EEG as indicators of mental states.

How to Cite

Dan, A., & Reiner, M. (2017). EEG Based Analysis of Cognitive Load Enhance Instructional Analysis. JEDM | Journal of Educational Data Mining, 9(2), 31-44. Retrieved from
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ANSARI, D., AND COCH, D. (2006). Bridges over troubled waters: education and cognitive neuroscience. Trends in Cognitive Sciences, 10(4), 146–51.

ANSARI, D., DE SMEDT, B., AND GRABNER, R. H. (2012). Neuroeducation – A Critical Overview of An Emerging Field. Neuroethics, 5(2), 105–117.

ANTONENKO, P., PAAS, F., GRABNER, R., AND VAN GOG, T. (2010). Using Electroencephalography to Measure Cognitive Load. Educational Psychology Review, 22(4), 425–438.

BANNERT, M. (2002). Managing cognitive load — recent trends in cognitive load theory. Learning and Instruction, 12, 139–146.

BASAR, E., BASAR-EROGLU, C., KARAKAS, S., AND SCHÜRMANN, M. (1999). Oscillatory brain theory: a new trend in neuroscience. IEEE Engineering in Medicine and Biology Magazine : The Quarterly Magazine of the Engineering in Medicine and Biology Society, 18(3), 56–66. Retrieved from

BAŞAR, E., BAŞAR-EROGLU, C., KARAKAŞ, S., AND SCHÜRMANN, M. (2001). Gamma, alpha, delta, and theta oscillations govern cognitive processes. International Journal of Psychophysiology, 39(2-3), 241–248.

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.

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.

BUCCINO, G., VOGT, S., RITZL, A., FINK, G. R., ZILLES, K., FREUND, H., AND RIZZOLATTI, G. (2004). of Hand Actions : An Event-Related fMRI Study. Neuron, 42, 323–334.

CAIN, B. (2004). A Review of the Mental Workload Literature. In NATO RTO-TR-HFM-121-Part-II (pp. 4–1 – 4–34). Retrieved from

CAREW, T. J., AND MAGSAMEN, S. H. (2010). Neuroscience and education: an ideal partnership for producing evidence-based solutions to Guide 21(st) Century Learning. Neuron, 67(5), 685–8.

CHANDLER, P., AND SWELLER, J. (1991). Cognitive Load Theory and the Format of Instruction. Cognition and Instruction, 8(4), 293–332.

CHIK, D. (2013). Theta-alpha cross-frequency synchronization facilitates working memory control – a modeling study. SpringerPlus, 2(1), 14.

COOPER, G. (2008). Research into Cognitive Load Theory and Instructional Design at UNSW. Retrieved from 2/27/2008

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.

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.

DIRICAN, A. C., AND GÖKTÜRK, M. (2011). Psychophysiological measures of human cognitive states applied in human computer interaction. Procedia Computer Science, 3, 1361–1367.

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

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.

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.

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.

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

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.

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.

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

GOLDMAN, S. R. (2009). Explorations of relationships among learners, tasks, and learning. Learning and Instruction, 19(5), 451–454.

GOSWAMI, U., AND SZŰCS, D. (2011). Educational neuroscience: Developmental mechanisms: Towards a conceptual framework. NeuroImage, 57(3), 651–658.

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.

HANCOCK, P. A., AND MESHKATI, N. (1988). Human Mental Workload. Amsterdam: North-Holland.


HOLM, A., LUKANDER, K., KORPELA, J., SALLINEN, M., AND MÜLLER, K. M. I. (2009). Estimating brain load from the EEG. TheScientificWorldJournal, 9, 639–51.

IMMORDINO-YANG, M. H., AND FISCHER, K. W. (2010). Neuroscience Bases of Learning. In Learning and Cognition - Issues, Concepts, Types (pp. 310–316).

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.

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 Brain-Computer 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.

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.

LYSAGHT, R. J., HILL, S. G., DICK, A. O., PLAMONDON, B. D., LINTON, P. M., WIERWILLE, W. W., … WHERRY, R. J. (1989). Operator workload: Comprehensive review and evaluation of operator workload methodologies. United States Army Research Institute for the Behavioral Sciences, Technical Report. Alexandria, Virginia.

MAYER, R. E., AND MORENO, R. (2003). Nine Ways to Reduce Cognitive Load in Multimedia Learning. Educational Psychologist, 38(1), 43–52.

MCEVOY, L. K., SMITH, M. E., AND GEVINS, A. (1998). Dynamic cortical networks of verbal and spatial working memory: effects of memory load and task practice. Cerebral Cortex, 8(7), 563–74. Retrieved from

MICHELS, L., MOAZAMI-GOUDARZI, M., JEANMONOD, D., and SARNTHEIN, J. (2008). EEG alpha distinguishes between cuneal and precuneal activation in working memory. NeuroImage, 40(3), 1296–310.

MORRISON, G. R., AND ANGLIN, G. J. (2005). Research on cognitive load theory: Application to e-learning. Educational Technology Research and Development, 53(3), 94–104.

PAAS, F., RENKL, A., AND SWELLER, J. (2004). Cognitive Load Theory: Instructional Implications of the Interaction between Information Structures and Cognitive Architecture. Instructional Science, 32(1/2), 1–8.

PAAS, F., TUOVINEN, J. E., TABBERS, H., AND VAN GERVEN, P. W. M. (2003). Cognitive Load Measurement as a Means to Advance Cognitive Load Theory. Educational Psychologist, 38(1), 63–71.

PLASS, J. L., HEIDIG, S., HAYWARD, E. O., HOMER, B. D., AND UM, E. (2013). Emotional design in multimedia learning: Effects of shape and color on affect and learning. Learning and Instruction.

PARASURAMAN, R. (2003). Neuroergonomics: Research and practice. Theoretical Issues in Ergonomics Science, 4(1-2), 5–20.

PARASURAMAN, R., SHERIDAN, T. B., AND WICKENS, C. D. (2008). Situation Awareness, Mental Workload, and Trust in Automation: Viable, Empirically Supported Cognitive Engineering Constructs. Journal of Cognitive Engineering and Decision Making, 2(2), 140–160.

PENARANDA, B. N., AND BALDWIN, C. L. (2012, October 26). Temporal Factors of EEG and Artificial Neural Network Classifiers of Mental Workload. Proceedings of the Human Factors and Ergonomics Society Annual Meeting.

PICKUP, L., WILSON, J. R., SHARPIES, S., NORRIS, B., CLARKE, T., AND YOUNG, M. S. (2005). Fundamental examination of mental workload in the rail industry. Theoretical Issues in Ergonomics Science, 6(6), 463–482.

REBSAMEN, B., KWOK, K., AND PENNEY, T. B. (2011). Evaluation of Cognitive Workload from EEG During a Mental Arithmetic Task. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 55(1), 1342–1345.

REINER, M., AND GELFELD, T. M. (2014). Estimating mental workload through event-related fluctuations of pupil area during a task in a virtual world. International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology, 93(1), 38–44.

ROBERTS, D. M., TAYLOR, B. A., BARROW, J. H., ROBERTSON, G., BUZZELL, G., SIBLEY, C., … BALDWIN, C. L. (2010). EEG Spectral Analysis of Workload for a Part-task UAV Simulation. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 54(3), 200–204.

RUBIO, S., DÍAZ, E., MARTÍN, J., AND PUENTE, J. M. (2004). Evaluation of Subjective Mental Workload : A Comparison of SWAT , NASA-TLX , and Workload Profile Methods. Applied Psycholgy: An International Review, 53(1), 61–86.

RYU, K., AND MYUNG, R. (2005). Evaluation of mental workload with a combined measure based on physiological indices during a dual task of tracking and mental arithmetic. International Journal of Industrial Ergonomics, 35(11), 991–1009.

SAMMER, G. (1996). Working-memory load and dimensional complexity of the EEG. International Journal of Psychophysiology, 24(1-2), 173–182.

SMITH, M. E., GEVINS, A., BROWN, H., KARNIK, A., AND DU, R. (2001). Monitoring Task Loading with Multivariate EEG Measures during Complex Forms of Human-Computer Interaction. Human Factors: The Journal of the Human Factors and Ergonomics Society, 43(3), 366–380.

SWELLER, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285.

SWELLER, J. (2010). Element Interactivity and Intrinsic, Extraneous, and Germane Cognitive Load. Educational Psychology Review, 22(2), 123–138.

TABBERS, H. K., MARTENS, R. L., AND MERRIËNBOER, J. J. G. (2004). Multimedia instructions and cognitive load theory: Effects of modality and cueing. British Journal of Educational Psychology, 74(1), 71–81.

TEPLAN, M. (2002). Fundamentals of EEG Measurement. Measurement Science Review, 2(2), 1–11.

VAN MERRIËNBOER, J. J. G., and SWELLER, J. (2005). Cognitive Load Theory and Complex Learning: Recent Developments and Future Directions. Educational Psychology Review, 17(2), 147–177.

WARD, L. M. (2003). Synchronous neural oscillations and cognitive processes. Trends in Cognitive Sciences, 7(12), 553–559.

WHELAN, R. R. (2007). Neuroimaging of cognitive load in instructional multimedia. Educational Research Review, 2(1), 1–12.

WICKENS, C. D. (2008). Multiple Resources and Mental Workload. Human Factors: The Journal of the Human Factors and Ergonomics Society, 50(3), 449–455.

XIE, B., AND SALVENDY, G. (2000). Prediction of Mental Workload in Single and Multiple Tasks Environments. International Journal of Cognitive Ergonomics, 4(3), 213–242.

ZARJAM, P., EPPS, J., AND CHEN, F. (2011). Characterizing working memory load using eeg delta activity. 19th European Signal Processing Conference (EUSIPCO 2011), (Eusipco 2011), 1554–1558.