TY  - JOUR
PY  - 2016//
TI  - EEG-based driver fatigue detection using hybrid deep generic model
JO  - Conference proceedings - IEEE engineering in medicine and biology society
A1  - San, Phyo Phyo
A1  - Ling, Sai Ho
A1  - Chai, Rifai
A1  - Tran, Yvonne
A1  - Craig, Ashley
A1  - Nguyen, Hung
A1  - Phyo Phyo San, 
A1  - Sai Ho Ling, 
A1  - Rifai Chai, 
A1  - Tran, Yvonne
A1  - Craig, Ashley
A1  - Hung Nguyen, 
A1  - Craig, Ashley
A1  - Ling, Sai Ho
A1  - Nguyen, Hung
A1  - San, Phyo Phyo
A1  - Tran, Yvonne
A1  - Chai, Rifai
SP  - 800
EP  - 803
VL  - 2016
IS  - 
N2  - Classification of electroencephalography (EEG)-based application is one of the important process for biomedical engineering. Driver fatigue is a major case of traffic accidents worldwide and considered as a significant problem in recent decades. In this paper, a hybrid deep generic model (DGM)-based support vector machine is proposed for accurate detection of driver fatigue. Traditionally, a probabilistic DGM with deep architecture is quite good at learning invariant features, but it is not always optimal for classification due to its trainable parameters are in the middle layer. Alternatively, Support Vector Machine (SVM) itself is unable to learn complicated invariance, but produces good decision surface when applied to well-behaved features. Consolidating unsupervised high-level feature extraction techniques, DGM and SVM classification makes the integrated framework stronger and enhance mutually in feature extraction and classification. The experimental results showed that the proposed DBN-based driver fatigue monitoring system achieves better testing accuracy of 73.29 % with 91.10 % sensitivity and 55.48 % specificity. In short, the proposed hybrid DGM-based SVM is an effective method for the detection of driver fatigue in EEG.<p />  <p>Language: en</p>
LA  - en
SN  - 1557-170X
UR  - http://dx.doi.org/10.1109/EMBC.2016.7590822
ID  - ref1
ER  -