Abstract
ABSTRACTThis paper proposes a method of neural network based drowsiness detection with eyes open using power spectrum analysis and auto-regressive modelling. After the electroencephalogram measurements are complete, alertness, transient, and drowsy periods are classified according to alpha spectrum changes and alpha-blocking phenomena. Although the subject's eyes are open, alpha spectrum changes such as drowsiness patterns are detected. Consequently, drowsiness detection with eyes open is applied into the proposed system. The neural network based proposed method shows that LPC (linear predictive coding) coefficients are the proper feature vectors and average classification rate is about 92%.
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