Classification of EEG Spectrogram Image with ANN Approach for Brainwave Balancing Application

In this paper, an Artificial Neural Network (ANN) algorithm for classifying the EEG spectrogram images in brainwave is presented. Gray Level Co-occurrence Matrix (GLCM) texture feature from the EEG spectrogram images have been used as input to the system. The GLCM texture feature produced large dimension of feature, therefore the Principal Component Analysis(PCA) is used to reduce the feature dimension. The result shows that the proposed model is able to classify EEG spectrogram images with 77% to 84% accuracy for three classes of brainwave balancing application with an optimized ANN model in training by varying the neurons in the hidden layer, epoch, momentum rate and learning rate.

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