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IJIET 2016 Vol.6(8): 584-590 ISSN: 2010-3689
DOI: 10.7763/IJIET.2016.V6.756

Using Electrophysiological Features in Cognitive Tasks: An Empirical Study

Ramla Ghali, Sébastien Ouellet, and Claude Frasson

Abstract—Learners’ performances in intelligent tutoring systems or e-learning environments depend on various factors such as the nature of the task presented, their cognitive and affective abilities, etc. In this paper, we focus on studying in detail the variation of these different factors and more specifically the electroencephalogram (EEG metrics) and how they differ according to a category and a type of cognitive tasks. We also studied the possibility of predicting a learner’s performance using feature selection and multiple regressions. Primarily, results shows that learners’ scores could be predicted using in descending order the difficulty level of the task, the type of a task, the duration of a task and the EEG workload metric by building a multiple regression model that fit our data.

Index Terms—Cognitive tasks, EEG features, engagement, workload, distraction, multiple regression.

The authors are with the Université de Montréal, Département d’Informatique et de Recherche Opérationnelle, 2900 Chemin de la Tour, Montréal, H3C 3J7 Canada (e-mail: ghaliram@iro.umontreal.ca, sebouel@gmail.com, frasson@iro.umontreal.ca).


Cite: Ramla Ghali, Sébastien Ouellet, and Claude Frasson, "Using Electrophysiological Features in Cognitive Tasks: An Empirical Study," International Journal of Information and Education Technology vol. 6, no. 8, pp. 584-590, 2016.

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2022: 2.0), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net


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