1Research Scholar, Department of ECE, College of Engineering, Anna University, Guindy, Chennai, Tamil Nadu, India
2Scientist, Defence Bio-engineering and Electromedical Laboratory (DEBEL), DRDO, Bangalore, Karnataka, India
3Associate Professor
4M. Tech. Student, Department of ECE, College of Engineering, Anna University, Guindy, Chennai, Tamil Nadu, India
5Associate Professor, School of EEE, SASTRA Deemed to be University, Thanjavur, Tamil Nadu, India
6>Associate Professor, School of Electronics Engineering, Vellore Institute of Technology (VIT), Chennai, Tamil Nadu, India
*Corresponding Author: Mr. K. Mohanavelu Department of Electronics & Communication Engineering, College of Engineering, Guindy Anna University, Chennai, Tamil Nadu, India, Scientist, Defence Bio-engineering and Electromedical Laboratory (DEBEL), DRDO, Bangalore, Karnataka, India Email: mohanvelk@debel.drdo.in
Online published on 1 November, 2018.
Mental fatigue is a typical phenomenon in our everyday life, and is characterized as a condition of cortical deactivation. Mental deactivation produces performance degradation such as human failures, errors and health problems, thereby detaining the quality of life. Various physiological parameters obtained from biosignals have been identified as an indicator of fatigue. The main biosignals that help to detect the mental performance are Electrocardiogram (ECG), Electroencephalogram (EEG) and Electrooculogram (EOG). After acquiring these signals, they undergo various stages of processing which includes signal de-noising, feature extraction and classification for the efficient analysis of mental performance. The paper provides comprehensive review of various approaches involved in processing of biosignals to detect mental fatigue.
Mental Fatigue, Signal Processing, Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG)
