Key result
The proposed WpdAI-ICA algorithm performed better than relevant methods in removing severe artifacts from EEG data collected during virtual reality fear stimulation.
Why the study?
Studying emotions requires objective indicators such as EEG to avoid subjective judgment, but severe artifacts especially when eyes are open hinder analysis during VR fear stimulation.
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The proposed WpdAI-ICA algorithm effectively removes artifacts from EEG data, enabling objective measurement of fear emotion induced by Virtual Reality.
Virtual Reality (VR), which becomes more and more common in daily life, can affect human's emotion with its special visual environment. VR has been a tool to study emotion in psychology. At the same time, the study of emotion requires an objective indicator to avoid subjective judgment. One of the methods is to measure the EEG of humans, which reflects the real situation of the brain. In order to study the effects of VR on fear emotion, we conducted EEG data collection based on the VR roller coaster scene to stimulate fear. However, there exists severe artifacts in EEG data especially when the tester is opening eyes, which blocks the correct analysis of EEG. In this paper, we propose a new artifacts removal algorithm, named WpdAI-ICA, which performs better than relevant methods. With the recovered EEG data, we compare and analyze it in time and frequency domains, and show how VR affect emotion.
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Guo et al. (2019) studied Fear emotion. WpdAI-ICA algorithm vs. Relevant methods was evaluated on Artifact removal performance. The proposed WpdAI-ICA algorithm performed better than relevant methods in removing severe artifacts from EEG data collected during virtual reality fear stimulation.
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