eprintid: 11822
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/18/22
datestamp: 2023-11-10 03:26:21
lastmod: 2023-11-10 03:26:21
status_changed: 2023-11-10 01:16:13
type: conference_item
metadata_visibility: show
creators_name: Babiker, A.
creators_name: Faye, I.
creators_name: Malik, A.S.
creators_name: Sato, H.
title: Studying the effect of lecture content on students� EEG data in classroom using SVD
ispublished: pub
keywords: Biomedical engineering; Brain; Electroencephalography; Human computer interaction; Neurophysiology; Singular value decomposition; Support vector machines; Surveys, Brain activity; Classroom; Eeg datum; Human computer interfaces; Learning materials; Learning process; Singular values; Situational interest, Students
note: cited By 3; Conference of 2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 ; Conference Date: 3 December 2018 Through 6 December 2018; Conference Code:144644
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. © 2018 IEEE.
date: 2019
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062785773&doi=10.1109%2fIECBES.2018.8626664&partnerID=40&md5=629ab3f8d0f817e565e7d0522405fd67
id_number: 10.1109/IECBES.2018.8626664
full_text_status: none
publication: 2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 - Proceedings
pagerange: 200-204
refereed: TRUE
isbn: 9781538624715
citation:   Babiker, A. and Faye, I. and Malik, A.S. and Sato, H.  (2019) Studying the effect of lecture content on students� EEG data in classroom using SVD.  In: UNSPECIFIED.