bioRxivNeurosciencePreprint
Environmental Noise Alters Neural Regulation Without Behavioral Impairment: A Pilot EEG Study
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Abstract
Environmental noise is a pervasive stressor that impairs attention and increases arousal, whereas natural soundscapes are linked to restoration and improved well-being. This pilot study tested whether exposure to natural auditory environments can buffer the neural strain induced by noise. Twelve healthy adults completed five cognitive tasks (DotProbe, Stroop, GoNoGo, NBack, Visual Search) under three auditory conditions: Noise (urban traffic), Nature (ambient natural sounds), and Control (silence with earplugs), while 32-channel EEG recorded ongoing activity. Behavioral accuracy remained high across all tasks (89.9 to 99.5 %), differing only for the Nback, which improved under Nature relative to Noise and Control. In contrast, EEG revealed robust environment-dependent modulation. Mixed-effects models (FDR-corrected p < .05) identified 17 significant condition effects across frequency bands and cortical regions. Nature increased baseline-corrected{delta} , {beta}, and{gamma} power, elevated /{beta} ratios, and enhanced the Engagement Index in posterior networks, all signatures of relaxed yet alert cortical states. Noise, by contrast, amplified{delta} and{theta} activity and raised{theta} / ratios in parieto-occipital regions, indicating higher cognitive load and compensatory effort. Reliability analyses confirmed moderate within-condition stability of EEG measures (mean ICC = 0.53 to 0.61) and higher consistency for ratio-based indices. Together, these findings reveal that natural soundscapes promote efficient, low-effort neural organization, whereas urban noise elicits energetically costly activation despite preserved behavioral performance. The results establish electrophysiological markers of environmental stress and restoration, supporting biophilic design strategies for healthier and more cognitively sustainable work environments.
- organism: human
- assay: electronic health records
- research focus: benchmarking
- approach: statistical model
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