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The performance of a microsleep detection system was calculated in terms of its ability to detect the behavioural microsleep state (1-s epochs) from spectral features derived from 16-channel EEG sampled at 256 Hz. Best performance from a single classifier model was achieved using leaky integrator neurons on an echo state network (ESN) classifier with a mean phi correlation (ϕ) of 0.38 and accuracy...
This study investigates the classification ability of linear and nonlinear classifiers on biological signals using the electroencephalogram (EEG) and examines the impact of architectural changes within the classifier in order to enhance the classification. Consequently, artificial events were used to validate a prototype EEG-based microsleep detection system based around an echo state network (ESN)...
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