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EEG Signal Classification for Alcoholic Person using Neural Network


Praful V. Barekar,Rajeshree V. Ambulkar,Kavita R. Singh,Lalit B. Damahe,Shailesh D. Kamble,Shashank Gotarkar
Abstract

he Causes of Road accidents are increases day by day due to many reasons and one of them is person consume the alcohol while driving. These problems can be identified through the automation process and the existing literatures are available. Alcoholic and non alcoholic identification is a binary classification problem. The objective of this paper is to classify the EEG signals into control and alcoholic category using Spiking Neural Network. Izhikevich model of spiking neural network is used and order 2 daubechies wavelet is used to extract the notable features from the EEG signals. For the experimentation standard data set is used which contain measurement of 64 electrodes placed on the scalp having a sampling rate of 256 Hz. Result Shows that classification using spiking neural network having a good accuracy in comparison with neural network for classification of alcoholic and non alcoholic person.

Volume 11 | 08-Special Issue

Pages: 3112-3124