• Syed Abdul Irfan
• Mir Rahemath Ali
• Sameer Khan
• Mohammed Sadiq

Most of the methods to analysis alcoholic signals on machine learning base styles that cannot prize the deep concealed characteristics of Electroencephalogram (EEG) signals from different layers. Hence, this study aims to introduce a deep leaning- system that can automatically identify alcoholic EEG signals. To inquire, wither the person is alcoholic or not will detect using EEG signals for detection. CNN Algorithm has been used, It consist of three layers -an input layer, hidden layers, and an output layer. CNN are inspired by the architecture of the brain. Just like a neuron in the brain processes and transmits information throughout the body, artificial neurons or nodes in CNN take inputs, processes them and sends the result as output. The image of signals fed as input. The input layer accepts the image pixels as input in the form of arrays. In CNN, there could be multiple hidden layers, which perform feature extraction from the image by doing calculations. This will include convolution, pooling, rectified linear units, and fully connected layers. Convolution is the first layer that does feature extraction from an input image. The fully connected layer classifies the object and identifies it in the output layer. “CNN are feed forward networks in that information flow takes place in one direction only, from the inputs to the outputs.

Classification of EEG, Convolution network, Layers in CNN, Alcoholic signal, Deep learning, Google colaboratory

"Classification of Alcoholic EEG Signals Using Deep Learning Method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.c564-c572, June-2022, Available :http://www.jetir.org/papers/JETIR2206269.pdf

"Classification of Alcoholic EEG Signals Using Deep Learning Method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppc564-c572, June-2022, Available at : http://www.jetir.org/papers/JETIR2206269.pdf