Description

Electroencephalogram (EEG) is a most widely used signal to detect the various kind of disease and emotions of the person. This process is aims to predict the stress signal, using the wavelets is presented in this process. At First, the original input EEGs are applied to the pre-processing, based on the filtering method to remove the noise from the signal. Then the pre-processed signal is transformed into Alpha, Beta, Gamma, Theta and Delta. At second, the features are extracted by means of the Convolutional Neural Network algorithm. The feature vector are extracted to generate the test features from the signal. Then the classification step is used to classify the signal is stress or non-stress. Finally the performance of the process will be evaluated to identify the efficiency of the process