Improve affective learning with EEG approach

Xiaowei Li*, Qinglin Zhao, Li Liu, Hong Peng, Yanbing Qi, Chengsheng Mao, Zheng Fang, Quanying Liu, Bin Hu

*Corresponding author for this work

Research output: Contribution to journalArticle

21 Scopus citations


With the development of computer science, cognitive science and psychology, a new paradigm, affective learning, has emerged into e-learning domain. Although scientists and researchers have achieved fruitful outcomes in exploring the ways of detecting and understanding learners affect, e.g. eyes motion, facial expression etc., it sounds still necessary to deepen the recognition of learners affect in learning procedure with innovative methodologies. Our research focused on using bio-signals based methodology to explore learner's affect and the study was primarily made on Electroencephalography (EEG). After the EEG signals were collected from EEG equipment, we tidied the EEG data with signal processing algorithms and then extracted some features. We applied k-Nearest-Neighbor classifier and Naive Bayes classifier to these features to find out a combination, which may mostly contribute to reflect learners' affect, for example, Attention. In the classification algorithm, we presented a different way of using the Self-Assessment Manikin (SAM) model to classify and analyze learners attention, although the SAM was normally used for classifying emotions, for example, happiness etc. For the purpose of evaluating our findings, we also developed an affective learning prototype based on university e-learning web site. A real time EEG feedback window and an attention report were integrated into the system. The result of the experiment was encouraging and further discussion was also included in this paper.

Original languageEnglish (US)
Pages (from-to)557-570
Number of pages14
JournalComputing and Informatics
Issue number4
StatePublished - 2010
Externally publishedYes


  • Affective learning
  • Classification algorithm
  • EEG
  • Sam model

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computational Theory and Mathematics

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  • Cite this

    Li, X., Zhao, Q., Liu, L., Peng, H., Qi, Y., Mao, C., Fang, Z., Liu, Q., & Hu, B. (2010). Improve affective learning with EEG approach. Computing and Informatics, 29(4), 557-570.