Comparing combinations of EEG activity in train drivers during monotonous driving

Authors:
Budi Thomas Jap;Sara Lal;Peter Fischer
Affiliations:
Department of Medical and Molecular Biosciences, Faculty of Science, University of Technology, Building 4, Level 6, Broadway, Sydney, NSW 2007, Australia;Department of Medical and Molecular Biosciences, Faculty of Science, University of Technology, Building 4, Level 6, Broadway, Sydney, NSW 2007, Australia;Signal Network Technology, Lane Cove, NSW 2066, Australia
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 4
Cited 1

Active Facial Tracking for Fatigue Detection

WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Using Spectral Analysis to Extract Frequency Components from Electroencephalography: Application for Fatigue Countermeasure in Train Drivers

AUSWIRELESS '07 Proceedings of the The 2nd International Conference on Wireless Broadband and Ultra Wideband Communications
Using EEG spectral components to assess algorithms for detecting fatigue

Expert Systems with Applications: An International Journal
Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients

Expert Systems with Applications: An International Journal

Uncorrelated fuzzy neighborhood preserving analysis based feature projection for driver drowsiness recognition

Fuzzy Sets and Systems

Quantified Score

Hi-index	12.05

Visualization

Abstract

This study investigated the changes in electroencephalography (EEG) activity in train drivers during a monotonous train-driving session. Four combinations of EEG activities were also compared to investigate the difference in performance of these equations. The four equations tested were equation 1 (@q/@b), equation 2 (@q/(@a+@b)), equation 3 ((@q+@a)/@b), and equation 4 ((@q+@a)/(@a+@b)). A total of fifty male train drivers were recruited to perform a 30-min monotonous train-driving task while 2-channels of EEG (frontal and temporal) were recorded. At the frontal site, significant differences were found for theta (p=0.045) and alpha (0.0001) activities, and at the temporal site, significant differences were found for delta (p=0.007) and theta (0.01) activities. For the average of frontal and temporal site activities, significant differences were found for delta (p=0.004), theta (p=0.001), and beta (p=0.048). Significant difference were found for temporal site for equation 1 (@q/@b) (p=0.04), and equation 4 ((@q+@a)/(@a+@b)) (p=0.02), and for the average of frontal and temporal site activities, significant differences were found for all four equations (equation 1 (p=0.001), equation 2 (p=0.006), equation 3 (p=0.04), and equation 4 (p=0.002)). These findings can be utilised as a potential fatigue indicator.