EEG Signals in Mental Fatigue Detection: A Comparing Study of Machine Learning Technics VS Deep Learning

Abstract

Mental fatigue is a complex disorganization that affects the human being efficiency in work and daily activities as (driving, exercising etc.). To discern that fatigue, the encephalography is routinely used, several automatic procedures have been deploying conventional approaches to support neurologists in mental fatigue detection episodes e.g. (sleepy vs normal). The aim of this work is the use of the EEG’s data to understand how the mental fatigue can affect the conductor’s behavior, lot of methods are involved to understand these data as machine learning approach and deep learning method. The data is organized as follow: EEG data of 10 normal people and other 10 people who are deprived from sleep, the recording time is 7 min in each session, and the experiment includes three sessions for each person, none of the volunteers n have a mental history and none of them are on medication. The main of this study is to compare the different methods for the analysis of EEG signals for the detection of fatigue, using machine learning and deep learning.