NETLAB: algorithms for pattern recognition
NETLAB: algorithms for pattern recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Multiple Object Class Detection with a Generative Model
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Statistics over features: EEG signals analysis
Computers in Biology and Medicine
EEG-based drivers' drowsiness monitoring using a hierarchical Gaussian mixture model
FAC'07 Proceedings of the 3rd international conference on Foundations of augmented cognition
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
The Journal of Machine Learning Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
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Vigilance level estimation can be used to prevent disastrous accident occurring frequently in high-risk tasks. Electroencephalograph (EEG) based Brain Computer Interface (BCI) is one of the most important tools for detecting one's brain electrical activities. Unfortunately, several problems including its sensitivity to artifacts, inaccurate labels and the great diversity of patterns within EEG signals present great challenges to predict vigilance level reliably. In this paper we propose an integrated approach to estimate vigilance level, which incorporates an automatically artifact removing preprocess, a novel vigilance labeling method and finally a Gaussian Mixed Model (GMM) to discover the underlying pattern of EEG signals. Extensive off-line experiments are conducted on 12 groups of data sets to show the effectiveness of our integrated approach in the real-time application. A reasonably high classification performance (88.46% over 12 data sets) is obtained with low delay by employing only one channel in the frontal lobe, which is in accordance with the conclusions of brain science and is of significance in practice.