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Engaging the brain: The impact of natural versus urban scenes using novel EEG methods in an experimental setting

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    Rights statement: Copyright © 2013 Jenny J. Roe et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Original languageEnglish
Pages (from-to)93-104
JournalJournal of Environmental Sciences
Volume1
Issue number2
DOIs
Publication statusPublished - 2013

Abstract

Background
Researchers in environmental psychology have consistently shown the restorative potential of natural – over urban - settings using video/photographic experiments in laboratory settings applying subjectively rated scales. But few studies have employed objective indicators of emotional response. This study investigates the use of electroencephalography (EEG) as a method to understand how the brain engages with natural versus (vs) urban settings – in tandem with subjective preferences.
Methods
Using Emotiv EPOC, a commercial and low-cost EEG recorder, participants (n=20) viewed a series of urban vs landscape scenes with proven reliability in restorative environments research. The equipment provided continuous recordings from 5 channels, labelled Excitement; Frustration; Engagement; Long Term (LT) Excitement (or arousal) and Meditation. Participants also rated the image set subjectively for valence (pleasure-displeasure), arousal (calm-excitement), attractiveness and willingness to visit the scene.
Results
Landscape scenes were consistently rated more positively on the preference scales (i.e. attractiveness, willingness to visit and valance ratings) (p<0001). Data reduction of the EEG output revealed two components: Arousal which correlated with urban scenes and Interest which correlated with landscape scenes (p<0.01). Latent class analysis was carried out to explore clusters – or sub groups – in the data and to identify significant emotional discriminators between the two sets of images. A two-cluster model produced the best fit, with image scene, and three of the EEG emotional parameters (i.e. excitement, LT excitement, and meditation) significantly discriminating between the two clusters (p<0.05). Landscape scenes were associated with greater levels of meditation and lower arousal (i.e. excitement) and the urban scenes with higher arousal.
Conclusion
It has been shown that EEG data in an experimental setting is sensitive to detecting emotional change from viewing different environmental settings, furthering the evidence base for a restorative effect of natural settings. We have established a novel method for measuring environment-mind interactions – a tool we have subsequently developed to establish the mood-enhancing benefits of walking in urban green space.

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