Analysis of alertness status of subjects undergoing the cortical auditory evoked potential hearing test

Authors:
Ahmed Al-Ani;Bram Van Dun;Harvey Dillon;Alaleh Rabie
Affiliations:
Faculty of Engineering and Information Technology, University of Technology, Sydney, Ultimo, NSW, Australia;National Acoustic Laboratories, Chatswood, Sydney, NSW, Australia;National Acoustic Laboratories, Chatswood, Sydney, NSW, Australia;Faculty of Engineering and Information Technology, University of Technology, Sydney, Ultimo, NSW, Australia
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Year:
2012

Citing 4
Cited 0

Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)

Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
The ANN-based computing of drowsy level

Expert Systems with Applications: An International Journal
Driver drowsiness detection with eyelid related parameters by Support Vector Machine

Expert Systems with Applications: An International Journal
Applying neural network analysis on heart rate variability data to assess driver fatigue

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we analyze the EEG rhythms of subjects undergoing the cortical auditory evoked potential (CAEP) hearing test. Investigation of the importance of the different EEG rhythms in terms of their capability in differentiating between the different alertness states when considering 64 channel EEG montage is conducted. This is followed by considering subsets that contain 2, 3, 4 as well as all 5 EEG rhythms. Finally, a feature subset selection method based on differential evolution (DE) that has particularly been proposed to deal with multi-channel signals is used to search for the best subset of EEG rhythms for the various channels.