EEG-Based Analysis for Learning through Virtual Reality Environment
Received Date: Jan 10, 2018 / Accepted Date: Jan 27, 2018 / Published Date: Feb 03, 2018
Recently, many researchers studied learning through VR environment in various fields. Their assessment tools were based on tests, quizzes and/or statistical analysis of questionnaires. This study is based on the analysis of EEG signals collected from the students’ brains directly to capture their feelings and engagement during the lecture in both traditional and VR methods of teaching.
To recognize the emotions of the students, the fine K-Nearest Neighbor (KNN) algorithm is used. To calculate the engagement score for a student, a well-known engagement score formula issued.
The participants chosen are students of Anatomy and Physiology course. All participants were subject to three sessions of EEG signal acquisition for both Real Lecture and Virtual Reality, each session is five-minutes long. For better accuracy, EEG signals were captured three times for each student in each lecturing method. Based on the data-analyzing methods applied, which are Dependent Paired Samples T-Test and Independent Paired Samples T-Test, positive emotions in a real lecture are better than positive emotions in a VR-Lecture. However, the engagement score in both classes was approximately the same.
Keywords: Electroencephalography (EEG); Emotion classification; Engagement; Education comparison; Virtual reality (VR); Physiology and anatomy
Citation: Alwedaie SA, Khabbaz HA, Hadi SR, Al-Hakim R (2018) EEG-Based Analysis for Learning through Virtual Reality Environment. J Biosens Bioelectron 9: 249. Doi: 10.4172/2155-6210.1000249
Copyright: © 2018 Alwedaie SA, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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