Diana Guadalupe González-Rodríguez
ABSTRACT
Numerous studies are tackling mental fatigue due to the high number of accidents caused by mental fatigue. The requirement of real-time detection allows that EEG measurement is one of the most feasible options because it is non-invasive and low cost, against other techniques. In this paper, it is proposed a drivers’ mental fatigue detection through EEG signals using the Hurst exponent, because of the simple calculations to obtain it and the fractal nature of EEG signals, and it was compared with the event-related desynchronization/ synchronization (ERD/ERS). Frontocentral (FC3) right and left parietal (P3 and P4) regions in the alpha-band, which are regions where mental fatigue is detected, were analyzed. The task was divided into 3 blocks at 2, 25, and 40 minutes. The results for ERD/ERS in block 2 (25 minutes) showed a desynchronization in electrode FC3 and synchronization in electrodes P3 and P4, these changes indicate the subjects presented mental fatigue in that block. The results using Hurst’s exponent showed for block 2 that persistence decays at electrode FC3, while for electrodes P3 and P4 persistence increases. The results found for ERD/ERS and the Hurst exponent showed a positive correlation in block 2, which is where the first symptoms of mental fatigue appear. It is concluded that the Hurst exponent can be a potential tool that can be used as an indicator to detect mental fatigue.
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Index Terms
- EEG-Based Drivers Mental Fatigue Detection Using ERD/ERS and Hurst Exponent
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