Studying the effect of lecture content on students’ EEG data in classroom using SVD

Areej Babiker, Ibrahima Faye, Aamir Saeed Malik, Hiroki Satou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The recent innovation in technology led to huge advancement in Human-Computer Interface (HCI) systems and applications. Detection of brain activities is the vital element in these applications. This paper is employing Singular Value Decomposition (SVD) on EEG data acquired simultaneously from students in classroom to detect the changes of brain activities during learning process. Situational interest of subjects and the learning materials were evaluated through questionnaires. After preprocessing and segmentation of the data, SVD was applied on each segment separately. The 2-norms of the singular values were compared to the subject baseline and the overall result complied with the questionnaire result. Furthermore, feeding these features to Support Vector Machine (SVM) classifier achieved 83.3% accuracy in differentiating between high and low situationally interested students. It is therefore, suggested that SVD could be applied successfully to detect changes in students’ brain activities in classrooms.

Original languageEnglish
Title of host publication2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages200-204
Number of pages5
ISBN (Electronic)9781538624715
DOIs
Publication statusPublished - 2019 Jan 24
Event2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 - Kuching, Malaysia
Duration: 2018 Dec 32018 Dec 6

Publication series

Name2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 - Proceedings

Conference

Conference2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018
CountryMalaysia
CityKuching
Period18/12/318/12/6

Keywords

  • Classroom
  • EEG
  • KNN
  • Situational interest
  • SVD
  • SVM

ASJC Scopus subject areas

  • Biomedical Engineering
  • Medicine (miscellaneous)
  • Health Informatics
  • Instrumentation

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