Identification Of Drivers Drowsiness Based On Features Extracted From EEG Signal Using Neural Network
Electroencephalogram (EEG) is an electrophysiological monitoring method to record electrical activity of the brain. The brain waves are produced when electric current passes through the brain and that is being recorded by the Electroencephalogram. After taking the EEG signal the signal is preprocessed by using the Butterworth filter to remove noise and low quality signals, then the signal is segmented with the help of Hilbert Huang Transform(HHT) so that the signals are segmented into five primary frequency band (delta, theta, alpha, beta, gamma). Finally the EEG signals were classified based on the statistical features obtained from the different segments of the EEG signals using Neural Network Classifier. The Neural Network is a pattern recognition tool that identifies the type of the signals by analyzing the patterns of the features. The identification of the fatigue based on the features extracted is more efficient compared to the other feature extraction methods employed for the analysis of the signals.