Deep Learning on VR-Induced Attention

Li, G. and Khan, M. A. (2019) Deep Learning on VR-Induced Attention. In: 2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), San Diego, CA, USA, 9-11 Dec 2019, pp. 163-1633. ISBN 9781728156040 (doi: 10.1109/AIVR46125.2019.00033)

Abstract

Some evidence suggests that virtual reality (VR) approaches may lead to a greater attentional focus than experiencing the same scenarios presented on computer monitors. The aim of this study is to differentiate attention levels captured during a perceptual discrimination task presented on two different viewing platforms, standard personal computer (PC) monitor and head-mounted-display (HMD)-VR, using a well-described electroencephalography (EEG)-based measure (parietal P3b latency) and deep learning-based measure (that is EEG features extracted by a compact convolutional neural network-EEGNet and visualized by a gradient-based relevance attribution method-DeepLIFT). Twenty healthy young adults participated in this perceptual discrimination task in which according to a spatial cue they were required to discriminate either a "Target" or "Distractor" stimuli on the screen of viewing platforms. Experimental results show that the EEGNet-based classification accuracies are highly correlated with the p values of statistical analysis of P3b. Also, the visualized EEG features are neurophysiologically interpretable. This study provides the first visualized deep learning-based EEG features captured during an HMD-VR-based attentional task

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Li, Dr Gang
• Authors: Li, G., and Khan, M. A.
• College/School: College of Science and Engineering > School of Psychology
• ISBN: 9781728156040
• Copyright Holders: Copyright © 2019 IEEE
• First Published: First published in 2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy