Mental Fatigue Assessment using recording Brain Signals: Electroencephalography

Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features

Driver fatigue has become one of the major causes of traffic accidents, and is a complicated physiological process. However, there is no effective method to detect driving fatigue. Electroencephalography (EEG) signals are complex, unstable, and non-linear; non-linear analysis methods, such as entropy, maybe more appropriate. This study evaluates a combined entropy-based processing method of EEG...

Mental Fatigue and Its Effect on the Performance of the Faculty of Health Staff Using Electroencephalographic Signals

Background and Objective: Mental fatigue usually occurrs as a result of long-term cognitive activities. Mental fatigue could have important effects on the daily lives of healthy people. Therefore, the purpose of this study was to estimate mental fatigue and its impact on staff performance. Materials and Methods: This descriptive analytic study was performed on 10 staff with a mean age of 36±6....

Mental Fatigue Measurement Using EEG

1.1 Background We live in a highly technological and information-oriented society. The use of computers in modern society is omnipresent. They are used for innumerable applications in various sizes and forms. Over the past 20 years, the personal computer has become widely used, both in the office and at home. People use computers to write documents, maintain databases, manage finances, draw dia...

Combining Brain Recording Technologies: Using Brain Surfaces

The surfaces of the human cortex like the boundary between the grey and white matter or the pial surface, which separates the grey matter from the cerebral spinal fluid, are strongly folded. The surface areas can be estimated from structural MRI scans and turn out to be 1994cm for the pial surface and 1741cm for the grey-white matter boundary for the subject shown here. A comparison to the surf...

Electrophysiological Recording and Imaging of Neuronal Signals in Brain Slices

Assessment of Muscle Fatigue from TF Distributions of SEMG Signals

Assessment of muscle fatigue involves the creation of indices based on the estimation of variables such as the instantaneous frequency (IF) and the instantaneous amplitude (IA), which can be estimated from a time-frequency (TF) distribution of a surface electromyographic signal (SEMG). Since during muscle fatigue there is a decrease of the IF and an increase of the IA, slopes from these paramet...