Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Feature Fusion for the Detection of Microsleep Events
Journal of VLSI Signal Processing Systems
An Acoustic Framework for Detecting Fatigue in Speech Based Human-Computer-Interaction
ICCHP '08 Proceedings of the 11th international conference on Computers Helping People with Special Needs
The performance of LVQ based automatic relevance determination applied to spontaneous biosignals
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Alertness assessment using data fusion and discrimination ability of LVQ-networks
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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A novel approach for Microsleep Event detection is presented. This is achieved based on multisensor electroencephalogram (EEG) and electrooculogram (EOG) measurements recorded during an overnight driving simulation task. First, using video clips of the driving, clear Microsleep (MSE) and Non-Microsleep (NMSE) events were identified. Next, segments of EEG and EOG of the selected events were analyzed and features were extracted using Power Spectral Density and Delay Vector Variance. The so obtained features are used in several combinations for MSE detection and classification by means of populations of Learning Vector Quantization (LVQ) networks. Best classification results, with test errors down to 13%, were obtained by a combination of all the recorded EEG and EOG channels, all features, and with feature relevance adaptation using Genetic Algorithms.