Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning
A drowsiness detection system based on EEGs and eyelid movements is proposed. Nonlinear features are extracted and fused from EEG wavelet sub-bands. An efficient detector extremely learning machine is employed. The proposed method achieves high detection accuracy and fast computation speed.
nonlinear features drowsiness detection eyelid movements electroencephalogram (eeg) wavelet decomposition extreme learning machine (elm)