Deep Review of Machine Learning Techniques on Detection of Drowsiness Using EEG Signal

Abstract

Electroencephalogram (EEG) is used to analyze the state of the brain. One of the critical states of the brain is drowsiness. Physical, mental tiredness, and unconsciousness are some of the reasons for drowsiness. Drowsiness state may lead to fatal crashes, severe injury, and property damage; sometimes, it can be analyzed and detected by using EEG. Analyzing EEG signals is complicated and tedious, so an automated diagnosis is required to interpret these signals effectively. In recent years, finding drowsy feeling while working has become an important research area. In this paper, the authors reviewed various drowsiness detection techniques in the literature and analyzed the performance of 15 different machine learning algorithms for the self-acquired feature set from the EEG.

KEYWORDS:

• Classification
• Drowsiness
• Drowsiness Detection
• Electroencephalogram (EEG)
• Features
• Scaling

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Notes on contributors

B. Venkata Phanikrishna

Venkata Phanikrishna Balam received his doctorate in computer science and engineering from the National Institute of Technology Rourkela, India, in 2021. His master's degree (MTech) was completed in Computer Science and Technology from Andhra University, Visakhapatnam, India, in 2013. He completed his graduation (BTech) degree in Information Technology from Jawaharlal Nehru Technological University-Kakinada (JNTUK) at Kakinada, India, in 2010. At present, Venkata Phanikrishna is working as an assistant professor Sr G-I in the School of Computer Science and Engineering, Vellore Institute of Technology Vellore, India. Further, his research interests include bio-signal processing, pattern recognition, computer vision, the Internet of Things, and machine learning. Corresponding author. Email: [email protected]; [email protected]

Allam Jaya Prakash

Allam Jaya Prakash received his MTech degree from the Department of Electronics and Communication Engineering, JNTUK Kakinada. His research areas include ECG signal processing, pattern recognition, and machine learning. He is currently working as a research fellow in the Department of ECE, National Institute of Technology, Rourkela, Odisha, India. Email: [email protected]

Chinara Suchismitha

Chinara Suchismita is a professor in computer science and engineering, National Institute of Technology Rourkela, Odisha, India. She completed her PhD in computer science and engineering from National Institute of Technology Rourkela. She has published more than 50 research papers in national and international journals and conferences; her research interests are in the area of networking, IoT, and digital signal processing. Email: [email protected]