eprintid: 7998
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/79/98
datestamp: 2023-11-09 16:19:50
lastmod: 2023-11-09 16:19:50
status_changed: 2023-11-09 16:10:55
type: article
metadata_visibility: show
creators_name: Bamatraf, S.
creators_name: Hussain, M.
creators_name: Aboalsamh, H.
creators_name: Qazi, E.-U.-H.
creators_name: Malik, A.S.
creators_name: Amin, H.U.
creators_name: Mathkour, H.
creators_name: Muhammad, G.
creators_name: Imran, H.M.
title: A System for True and False Memory Prediction Based on 2D and 3D Educational Contents and EEG Brain Signals
ispublished: pub
keywords: Electrophysiology; Forecasting; Support vector machines, Classification approach; Discriminative features; Educational contents; Healthy individuals; Learning and memory; Learning materials; Prediction accuracy; Short term memory, Electroencephalography, adolescent; adult; brain; brain mapping; defense mechanism; electroencephalogram; electroencephalography; female; human; learning; male; pattern recognition; physiology; recall; receiver operating characteristic; short term memory; support vector machine; young adult, Adolescent; Adult; Brain; Brain Mapping; Brain Waves; Electroencephalography; Female; Humans; Learning; Male; Memory, Short-Term; Mental Recall; Pattern Recognition, Visual; Repression, Psychology; ROC Curve; Support Vector Machine; Young Adult
note: cited By 6
abstract: We studied the impact of 2D and 3D educational contents on learning and memory recall using electroencephalography (EEG) brain signals. For this purpose, we adopted a classification approach that predicts true and false memories in case of both short term memory (STM) and long term memory (LTM) and helps to decide whether there is a difference between the impact of 2D and 3D educational contents. In this approach, EEG brain signals are converted into topomaps and then discriminative features are extracted from them and finally support vector machine (SVM) which is employed to predict brain states. For data collection, half of sixty-eight healthy individuals watched the learning material in 2D format whereas the rest watched the same material in 3D format. After learning task, memory recall tasks were performed after 30 minutes (STM) and two months (LTM), and EEG signals were recorded. In case of STM, 97.5 prediction accuracy was achieved for 3D and 96.6 for 2D and, in case of LTM, it was 100 for both 2D and 3D. The statistical analysis of the results suggested that for learning and memory recall both 2D and 3D materials do not have much difference in case of STM and LTM. © 2016 Saeed Bamatraf et al.
date: 2016
publisher: Hindawi Limited
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954534490&doi=10.1155%2f2016%2f8491046&partnerID=40&md5=87cf5f3defbe6f6ccaa15a6c4e1ad1ea
id_number: 10.1155/2016/8491046
full_text_status: none
publication: Computational Intelligence and Neuroscience
volume: 2016
refereed: TRUE
issn: 16875265
citation:   Bamatraf, S. and Hussain, M. and Aboalsamh, H. and Qazi, E.-U.-H. and Malik, A.S. and Amin, H.U. and Mathkour, H. and Muhammad, G. and Imran, H.M.  (2016) A System for True and False Memory Prediction Based on 2D and 3D Educational Contents and EEG Brain Signals.  Computational Intelligence and Neuroscience, 2016.   ISSN 16875265