EEG visual and non- Visual learner classification using LSTM recurrent neural networks

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 f...

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Main Authors: Jawed, S., Amin, H.U., Malik, A.S., Faye, I.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.23543 /
Published: Institute of Electrical and Electronics Engineers Inc. 2019
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062783413&doi=10.1109%2fIECBES.2018.08626711&partnerID=40&md5=86f34114a4e941db77089975febaa9d4
http://eprints.utp.edu.my/23543/
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id utp-eprints.23543
recordtype eprints
spelling utp-eprints.235432021-08-19T07:57:18Z EEG visual and non- Visual learner classification using LSTM recurrent neural networks Jawed, S. Amin, H.U. Malik, A.S. Faye, I. 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. Institute of Electrical and Electronics Engineers Inc. 2019 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062783413&doi=10.1109%2fIECBES.2018.08626711&partnerID=40&md5=86f34114a4e941db77089975febaa9d4 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. http://eprints.utp.edu.my/23543/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description 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.
format Conference or Workshop Item
author Jawed, S.
Amin, H.U.
Malik, A.S.
Faye, I.
spellingShingle Jawed, S.
Amin, H.U.
Malik, A.S.
Faye, I.
EEG visual and non- Visual learner classification using LSTM recurrent neural networks
author_sort Jawed, S.
title EEG visual and non- Visual learner classification using LSTM recurrent neural networks
title_short EEG visual and non- Visual learner classification using LSTM recurrent neural networks
title_full EEG visual and non- Visual learner classification using LSTM recurrent neural networks
title_fullStr EEG visual and non- Visual learner classification using LSTM recurrent neural networks
title_full_unstemmed EEG visual and non- Visual learner classification using LSTM recurrent neural networks
title_sort eeg visual and non- visual learner classification using lstm recurrent neural networks
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2019
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062783413&doi=10.1109%2fIECBES.2018.08626711&partnerID=40&md5=86f34114a4e941db77089975febaa9d4
http://eprints.utp.edu.my/23543/
_version_ 1741196692558970880
score 11.62408

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