The Effect of Noise Exposure on Cognitive Performance and Brain Activity Patterns
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2021.12.1.24Keywords:
Noise; Cognitive Performance; Attention; Brain Activity; ElectroencephalogramDimensions Badge
Issue
Section
It seems qualitative measurements of subjective reactions are not appropriate indicators to assess the effect of noise on cognitive performance. In this study, quantitative and combined indicators were applied to study the effect of noise on cognitive performance. A total of 54 young subjects were included in this experimental study. The participants’ mental workload and attention were evaluated under different levels of noise exposure including, background noise, 75, 85 and 95 dBA noise levels. The study subject’s EEG signals were recorded for 10 minutes while they were performing the IVA test. The EEG signals were used to estimate the relative power of their brain frequency bands.Abstract
Results revealed that mental workload and visual/auditory attention is significantly reduced when the participants are exposed to noise at 95 dBA level (P < 0.05). Results also showed that with the rise in noise levels, the relative power of the Alpha band increases while the relative power of the Beta band decreases as compared to background noise. The most prominent change in the relative power of the Alpha and Beta bands occurs in the occipital and frontal regions of the brain respectively.
The application of new indicators, including brain signal analysis and power spectral density analysis, is strongly recommended in the assessment of cognitive performance during noise exposure. Further studies are suggested regarding the effects of other psychoacoustic parameters such as tonality, noise pitch (treble or bass) at extended exposure levels.
How to Cite
Downloads
Similar Articles
- S. Gaherwal, M.M. Prakash, V. Sharma, STUDY OF INHIBITORY EFFECT OF EUCALYPTUS FRUIT EXTRACT AGAINST DIFFERENT BACTERIA , The Scientific Temper: Vol. 1 No. 01 (2010): The Scientific Temper
- Kowsalya Ramasamy, Thiyagarajan Krishnan, Performance analysis of RF substrate materials in ISM band antenna applications , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Dhabha Nehal Hitendrabhai, Sudhakar S, Effect of multidirectional plyometric training along with core strengthening among tennis players on dynamic balance, vertical jump performance and agility , The Scientific Temper: Vol. 16 No. 07 (2025): The Scientific Temper
- SHILPENDRA KOUR, REKHA KHANDAL, RASHMI TRIPATHI, EVALUATION OF LEAF EXTRACTS OF DIFFERENT MEDICINAL PLANTS FOR POTENTIAL ANTIBACTERIAL ACTIVITY AND PRELIMINARY PHYTOCHEMICAL ANALYSIS , The Scientific Temper: Vol. 3 No. 1&2 (2012): The Scientific Temper
- KAPIL KHULBE, SURESH C. SATI, ANTIBACTERIAL POTENTIAL EVALUATION OF RHIZOME EXTRACTS OF BERGINIA CILIATA (HAW.) STERNB , The Scientific Temper: Vol. 2 No. 1&2 (2011): The Scientific Temper
- Merlin Sofia S, D. Ravindran, G. Arockia Sahaya Sheela, Clean Balance-Ensemble CHD: A Balanced Ensemble Learning Framework for Accurate Coronary Heart Disease Prediction , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
- Y. Mohammed Iqbal, M. Mohamed Surputheen, S. Peerbasha, Swarm intelligence-driven HC2NN model for optimized COVID-19 detection using lung imaging , The Scientific Temper: Vol. 16 No. 03 (2025): The Scientific Temper
- Seema Rani Sarraf, S.N. Dubey, STRESS AND ACADEMIC ACHIEVEMENT IN RELATION TO DURATION OF SLEEP AND COURSE , The Scientific Temper: Vol. 8 No. 1&2 (2017): The Scientific Temper
- V. Karthikeyan, C. Jayanthi, Advancements in image quality assessment: a comparative study of image processing and deep learning techniques , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Engida Admassu, Classifying enset based on their disease tolerance using deep learning , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.

