GLCM texture feature reduction for EEG spectrogram image using PCA
Mahfuzah Mustafa, Mohd Nasir Taib, Zunairah Hj Murat, Sahrim Lias · 2010
In Electroencephalography (EEG) research, the analysis using its time or frequency signals are very popular. However, it has been shown elsewhere, that any feature rich signals can be examined using time-frequency components. This paper proposes a new technique of extracting Gray-level Co-occurrence Matrices (GLCM) texture via time-frequency analysis of EEG signals. The output of this technique produces a big feature matrix and it is reduced by applying Principal Components Analysis (PCA). The results of this experiment shows that EEG signals can be analysed or described using five major components of the GLCM.