INFORMATION AND MEMORY-BASED ANALYSIS FOR DECODING OF THE HUMAN LEARNING BETWEEN NORMAL AND VIRTUAL REALITY (VR) CONDITIONS
Hamidreza Namazi,
Mohammad Hossein Babini,
Kamil Kuca and
Ondrej Krejcar
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Hamidreza Namazi: College of Engineering and Science, Victoria University, Melbourne, Australia†Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czechia
Mohammad Hossein Babini: College of Engineering and Science, Victoria University, Melbourne, Australia
Kamil Kuca: ��Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czechia
Ondrej Krejcar: ��Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czechia
FRACTALS (fractals), 2021, vol. 29, issue 03, 1-8
Abstract:
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.
Keywords: Virtual Reality (VR); Brain; Memory; Hurst Exponent; Information; Shannon Entropy (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:wsi:fracta:v:29:y:2021:i:03:n:s0218348x21501632
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DOI: 10.1142/S0218348X21501632
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