analysis of eeg signal of drowsiness driving using chaotic features and statistical tests

electro encephalography is one of the reliable sources to detect sleep onset while driving. in this study we have tried to demonstrate that sleepiness and alertness signals are separable with an appropriate margin by extracting suitable features. so first of all we have recorded eeg signals from 10 volunteers. they were obliged to be sleep deprived about 20 hours before the test. we recorded the signals when subjects did a virtual driving game. they tried to pass some barriers that were shown on monitor. process of recording was ended after 45 minutes. then after preprocessing of recorded signals, we labeled them by drowsiness and alertness, by using times associated with pass times of the barriers or crash times to them. then we extracted some chaotic features (include higuchi's fractal dimension and petrosian's fractal dimension) and logarithm of energy of signal. by applying two tailed t-test, we have shown that these features can create 95% significance level of difference between drowsiness and alertness in each eeg channels.