Abstract
The purpose of this paper is to analysis EEG spectrogram image using Artificial Neural Network (ANN) for brainwave balancing application. Time-frequency approach or spectrogram image processing technique is used to analyze EEG signals. The Gray Level Co-occurrence Matrix (GLCM) texture feature was extracted from spectrogram image and passed through Principal components analysis (PCA) to reduce the feature dimension. The experimental result shows that ANN was able to analysis EEG spectrogram images with an optimized model in training by varying neurons in the hidden layer, learning rate and momentum.
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