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In today's scenario, more and more people are required to travel back and forth to various places. With the increasing vehicular population and their movements on the roads, accidents are also steadily increasing. It has become a nightmare for the authorities to prevent /reduce such fatal accidents on the roads. But the authorities' efforts are in vain. It is shocking to know the study results that around 50% of the road accidents are owing to drunken driving all over the world [1][2]. Any mechanism or device to reduce such deaths will be of great help. Drunken driving and its subsequent catastrophe can be avoided by monitoring the EEG of the driver. The power of the EEG signal in frontal region decreases with the increase in the amount of alcohol intake, and the power of the EEG signal in central, occipital region increases. Therefore, power spectral density can be used as a parameter to differentiate EEG of alcoholic from nonalcoholic, thereby reducing drunken driving. Results are presented to support this approach.

Electroencephalography (EEG) is the process of recording the complex activity of the brain in the form of signals. EEG primarily has delta, theta, alpha, beta and gamma frequency bands whose presence and strength describes changes in brain under different kinds of activities. On the other hand alcohol consumption leads to depression and confusion which reduces the activity of the nervous system thereby affecting the brain. Alcoholics are identified from normal persons by multi-resolution and multi-scale analysis of EEG. In our research, EEG is decomposed into sub frequency bands using wavelet. The effect of alcohol on each of these wave bands is identified using power spectral density analysis. These evident variations in EEG are manifested due to depression in brain activity caused by intake of alcohol. The first order and second order statistical measures of the EEG signal are selected as features. Classifiers such as Bayes, Naive Bayes, radial basis function network (RBFN), multilayer perceptron (MLP) and extreme learning machine (ELM) are used for classification. Results show that our proposed EEG analysis acts as an effective bio-marker for differentiating alcoholics from non-alcoholics and extreme learning machine provides higher classification efficiency (87.6%) compared to other classifiers used.

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