Effect of auditory noise circumstance on visual images encoding based electroencephalography analysis

ABSTRACT

Auditory stimulation may be beneficial or detrimental to human cognitive and memory processing. Numerous studies have been conducted to investigate the influence of various sounds on human performance. The current research discovers the impact of auditory white noise on the memory performance of 60 college students using different difficulty levels in visual object-number pair assessment. Memory perfomance is accessed based on the assessment recall scores and brain activities. An electroencephalography (EEG) device measures and records the brain's electrical activity during the experimental session. Nine EEG channel locations are selected for brain activities analysis. The raw EEG dataset is processed using wavelet methods of stationary wavelet transform (SWT) and discrete wavelet transfrom (DWT) using the Daubechies (db) function to eliminate the artifact components and extract the required EEG features. The result indicated that the college student remembered the object-number pair assessment better under auditory white noise rather than no audio condition. This contributed from the activation of alpha, theta, gamma, and beta activities that increased the subject's attention, alertness, and sensory processing during memorizing. Therefore, it is suggested that the auditory white noise should be listened to during remembering and learning visual items, which can boost memory performance.

GRAPHICAL ABSTRACT

KEYWORDS:

• Auditory stimuli
• white noise
• visual items
• brain rhythm
• electroencephalography

Acknowledgement

The authors would like to acknowledge the financial support provided by the Malaysia Ministry of Higher Education and Universiti Teknologi Malaysia under UTMER grant Q.J130000.3851.20J75. One of the authors, Syarifah Noor Syakiylla Binti Sayed Daud is a Researcher of Universiti Teknologi Malaysia under the Post-Doctoral Fellowship Scheme (Q.J130000.21A2.05E52) for the Project: ‘Brain Features Extraction using Wavelet Transform Approach for Detection of Visual Memory Improvement and Stress Reduction’.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Malaysia Ministry of Higher Education and Universiti Teknologi Malaysia: [Grant Number PDRU grant Q.J130000.21A2.05E52]; Malaysia Ministry of Higher Education and Universiti Teknologi Malaysia: [Grant Number UTMER grant Q.J130000.3851.20J75].

Notes on contributors

Syarifah Noor Syakiylla Sayed Daud

Syarifah Noor Syakiylla Sayed Daud received her Bachelor of Engineering (Electrical - Medical Electronics) as well as Master of Philosophy (Electrical Engineering) from UTM, Skudai. She obtained her Ph.D. in Chemical Engineering specialization in fuel cell also from UTM in 2021. She published a number of papers in preferred Journals and participated in several national and international conferences. Her research interests are biomedical signal processing and fuel cells.

Rubita Sudirman

Rubita Sudirman received her Bachelor (Hons) and Masters degree from the University of Tulsa, USA and her Ph.D. in Electrical Engineering from Universiti Teknologi Malaysia (UTM). Currently she is a professor and certified professional engineer serving at the School of Electrical Engineering, Faculty of Engineering, UTM. Her current research interests include Applications of Soft Computing in Biomedical Engineering particularly in Speech Processing, EEG & EOG signal analysis, medical electronics, and rehabilitation engineering.