GLCM texture classification for EEG spectrogram image

Abstract

Over the past century, time based and frequency based is used for analyzing Electroencephalography (EEG) signals. EEG is a scientific tool for measure signal from human brain. This paper proposes a time-frequency approach or spectrogram image processing technique for analyzing EEG signals. Gray Level Co-occurrence Matrix (GLCM) texture feature were extracted from spectrogram image and then Principal components analysis (PCA) was employed to reduce the feature dimension. The purpose of this paper is to classify EEG spectrogram image using k-nearest neighbor algorithm (kNN) classifier. The result shows classification rate was 70.83% for EEG spectrogram image.