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.

Landscape scenes were consistently rated more positively on the preference scales (i.e. attractiveness, willingness to visit and valance ratings). Data reduction of the EEG output revealed two components: Arousal which correlated with urban scenes and Interest which correlated with landscape scenes. 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. Landscape scenes were associated with greater levels of meditation and lower arousal (i.e. excitement) and the urban scenes with higher arousal.

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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