EEG based Brain Alertness Monitoring by Statistical and Artificial Neural Network Approach

Abstract

Since several work requires continuous alertness like efficient driving, learning, etc. efficient measurement of the alertness states through neural activity is a crucial challenge for the researchers. This work reports a practical method to investigate the alertness state from electroencephalography (EEG) of the human brain. Here, we have proposed a novel idea to monitor the brain alertness from EEG signal that can discriminate the alertness state comparing resting state with a simple statistical threshold. We have investigated two different types of mental tasks: alphabet counting & virtual driving to monitor their alertness level. The EEG signals are acquired from several participants regarding alphabet counting and virtual motor driving tasks. A 9-channel wireless EEG system has been used to acquire their EEG signals from frontal, central, and parietal lobe of the brain. With suitable preprocessing, signal dimensions are reduced by principal component analysis and the features of the signals are extracted by the discrete wavelet transformation method. Using the features, alertness states are classified using the artificial neural network. Additionally, the relative power of responsible frequency band to alertness is analyzed with statistical inference. We have found that the beta relative power increases at a significant level due to alertness which is good enough to differentiate the alertness state from the control state. It is also found that the increment of beta relative power for virtual driving is much greater than the alphabet counting mental alertness. We hope that this work will be very helpful to monitor constant alertness for efficient driving and learning.

Authors and Affiliations

Md. Asadur Rahman, Md. Mamun Rashid, Farzana Khanam, Mohammad Khurshed Alam, Mohiuddin Ahmad

Keywords

Related Articles

Activity Based Learning Kits for Children in a Disadvantaged Community According to the Project “Vocational Teachers Teach Children to Create Virtuous Robots from Garbage”

This research was aimed to develop and evaluate the activity based learning kits for children in a disadvantaged community according to the project “Vocational Teachers Teach Children to Create Virtuous Robots from Garba...

A New Steganography Technique using JPEG Images

Steganography is a form of security technique that using ambiguity to hide a secret message within an ordinary message between senders and receivers. In this paper, we propose a new steganography technique for hiding dat...

A Parallel Simulated Annealing Algorithm for Weapon-Target Assignment Problem

Weapon-target assignment (WTA) is a combinatorial optimization problem and is known to be NP-complete. The WTA aims to best assignment of weapons to targets to minimize the total expected value of the surviving targets....

Diagnosing Coronary Heart Disease using Ensemble Machine Learning

Globally, heart disease is the leading cause of death for both men and women. One in every four people is afflicted with and dies of heart disease. Early and accurate diagnoses of heart disease thus are crucial in improv...

 A Survey of Automated Text Simplification

 Text simplification modifies syntax and lexicon to improve the understandability of language for an end user. This survey identifies and classifies simplification research within the period 1998-2013. Simplificatio...

Download PDF file
  • EP ID EP448900
  • DOI 10.14569/IJACSA.2019.0100157
  • Views 96
  • Downloads 0

How To Cite

Md. Asadur Rahman, Md. Mamun Rashid, Farzana Khanam, Mohammad Khurshed Alam, Mohiuddin Ahmad (2019). EEG based Brain Alertness Monitoring by Statistical and Artificial Neural Network Approach. International Journal of Advanced Computer Science & Applications, 10(1), 431-442. https://europub.co.uk/articles/-A-448900