Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Independent component analysis: algorithms and applications
Neural Networks
EEG-based subject- and session-independent drowsiness detection: an unsupervised approach
EURASIP Journal on Advances in Signal Processing
Computational intelligent brain computer interaction and its applications on driving cognition
IEEE Computational Intelligence Magazine
An EEG-based brain-computer interface for dual task driving detection
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
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The development of brain-computer interfaces (BCI) for multiple applications has undergone extensive growth in recent years. Since distracted driving is a significant cause of traffic accidents, this study proposes one BCI system based on EEG for distracted driving. The removal of artifacts and the selection of useful brain sources are the essential and critical steps in the application of electroencephalography (EEG)-based BCI. In the first model, artifacts are removed, and useful brain sources are selected based on the independent component analysis (ICA). In the second model, all distracted and concentrated EEG epochs are recognized with a self-organizing map (SOM). This BCI system automatically identified independent components with artifacts for removal and detected distracted driving through the specific brain sources which are also selected automatically. The accuracy of the proposed system approached approximately 90% for the recognition of EEG epochs of distracted and concentrated driving according to the selected frontal and left motor components.