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    Area of Science:

    • Environmental Neuroscience
    • Cognitive Neuroscience
    • Neuroscience

    Background:

    • Understanding environmental impacts on neural activity is crucial for cognitive research.
    • Previous research has not specifically analyzed post-walk neural changes in forest environments.

    Purpose of the Study:

    • To investigate the effects of forest versus urban walking on brain activity using electroencephalography (EEG).
    • To differentiate neural patterns associated with different environments using machine learning.

    Main Methods:

    • Collected EEG data from 30 participants before and after walks in forest and urban settings.
    • Performed spectral analysis on Theta, Alpha, Beta, and Gamma frequency bands.
    • Utilized Random Forest, Support Vector Machine, and Multi-Layer Perceptron classifiers to analyze EEG data.

    Main Results:

    • The Low Beta frequency band showed the highest classification accuracy (0.78) in distinguishing between forest and urban walking environments.
    • Distinct EEG patterns were observed after walks in natural versus urban settings.

    Conclusions:

    • This study is the first to demonstrate the neural effects of post-walk forest exposure.
    • Findings support the cognitive benefits of nature exposure and highlight the utility of EEG and machine learning in environmental neuroscience.