eprintid: 11824
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/18/24
datestamp: 2023-11-10 03:26:21
lastmod: 2023-11-10 03:26:21
status_changed: 2023-11-10 01:16:14
type: conference_item
metadata_visibility: show
creators_name: Jawed, S.
creators_name: Amin, H.U.
creators_name: Malik, A.S.
creators_name: Faye, I.
title: EEG visual and non- Visual learner classification using LSTM recurrent neural networks
ispublished: pub
keywords: Biomedical engineering; Electroencephalography; Feature extraction; Learning systems, Back-ground knowledge; Feature classification; Learning and memory; Learning Style; LSTM RNN; Recurrent neural network (RNN); University students; Visual learners, Long short-term memory
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 purpose of this study is to distinguish the visual learners from non-visual learners while learning, having no background knowledge of the contents. The learners are distinguished analysing their brain patterns. EEG data were recorded during learning and memory tasks using 128 channels machine from a sample of thirty -four healthy university students. The students were shown the animated learning content in video format for eight minutes. The brain waves were measured during learning task. The study characterizes and distinguishes between the visual learners and non-visual learners considering the extracted brain patterns. The wavelet features are computed for the recorded EEG and are filtered into alpha and beta sub bands. These features are then given as an input to the Long-Short Term Memory (LSTM) Recurrent neural network (RNN). Feature classification using LSTM Recurrent neural network has attained training accuracy of 89 and 85 for beta and alpha bands for Learning session 1(Learning 1), 86 and 87 for Learning session 2(Learning 2). © 2018 IEEE.
date: 2019
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062783413&doi=10.1109%2fIECBES.2018.08626711&partnerID=40&md5=86f34114a4e941db77089975febaa9d4
id_number: 10.1109/IECBES.2018.08626711
full_text_status: none
publication: 2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 - Proceedings
pagerange: 467-471
refereed: TRUE
isbn: 9781538624715
citation:   Jawed, S. and Amin, H.U. and Malik, A.S. and Faye, I.  (2019) EEG visual and non- Visual learner classification using LSTM recurrent neural networks.  In: UNSPECIFIED.