| ORIGINAL ARTICLE |
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| Year : 2023 | Volume
: 25
| Issue : 116 | Page : 36--54 |
Analysing the Change in Brain Waves due to Heterogeneous Road Traffic Noise Exposure Using Electroencephalography Measurements
Manish Manohare1, E. Rajasekar1, Manoranjan Parida2
1 Department of Architecture and Planning, Indian Institute of Technology, Roorkee, India
2 Department of Civil Engineering, Indian Institute of Technology, Roorkee, India
Correspondence Address:
Manish Manohare
Department of Architecture and Planning, Indian Institute of Technology, Roorkee, 247667
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/nah.nah_58_22
Road traffic is the major source of noise pollution leading to human health impacts in urban areas. This study presents the relation between changes in human brain waves due to road traffic noise exposure in heterogeneous conditions. The results are based on Electroencephalogram (EEG) data collected from 12 participants through a listening experience of traffic scenarios at 14 locations in New Delhi, India. Energetic, spectral and temporal characteristics of the noise signals are presented. The impact of noise events on spectral perturbations and changes in the relative power (RP) of EEG signals are evaluated. Traffic noise variations modulate the rate of change in α and θ EEG bands of temporal, parietal and frontal lobe of the brain. The magnitude of event-related spectral perturbation (ERSP) increases with each instantaneous increase in traffic noise, such as honking. Individual noise events impact the temporal lobe more significantly in quieter locations compared with noisy locations. Increase in loudness changes the RP of α band in frontal lobe. Increase in temporal variation due to intermittent honking increases the RP of θ bands, especially in right parietal and frontal lobe. Change in sharpness leads to variation in the RP of right parietal lobe in theta band. Whereas, inverse relation is observed between roughness and the RP of right temporal lobe in gamma band. A statistical relationship between noise indicators and EEG response is established.
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