Open AccessINFORMATION AND MEMORY-BASED ANALYSIS FOR DECODING OF THE HUMAN LEARNING BETWEEN NORMAL AND VIRTUAL REALITY (VR) CONDITIONS
Author(s) -
Hamidreza Namazi,
Mohammad Hossein Babini,
Kamil Kuča,
Ondřej Krejcar
Publication year - 2021
Publication title -
fractals
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.654
H-Index - 44
eISSN - 1793-6543
pISSN - 0218-348X
DOI - 10.1142/s0218348x21501632
Subject(s) - electroencephalography , hurst exponent , decoding methods , computer science , entropy (arrow of time) , virtual reality , artificial intelligence , speech recognition , pattern recognition (psychology) , psychology , mathematics , neuroscience , statistics , algorithm , physics , quantum mechanics
In this paper, we investigated the learning ability of students in normal versus virtual reality (VR) watching of videos by mathematical analysis of electroencephalogram (EEG) signals. We played six videos in the 2D and 3D modes for nine subjects and calculated the Shannon entropy of recorded EEG signals to investigate how much their embedded information changes between these modes. We also calculated the Hurst exponent of EEG signals to compare the changes in the memory of signals. The analysis results showed that watching the videos in a VR condition causes greater information and memory in EEG signals. A strong correlation was obtained between the increment of information and memory of EEG signals. These increments also have been verified based on the answers that subjects gave to the questions about the content of videos. Therefore, we can say that when subjects watch a video in a VR condition, more information is transferred to their brains that cause increments in their memory.