Proceedings of 2010 IEEE Student Conerence on Research and Development (SCOReD 2010), 13 - 14 Dec 2010, Putrajaya, Malaysia GLCM Texture Feature Reduction for EEG Spectrogram Image using PCA Mahzah Mutafa1,2 I Faculty of Electrical & Electronics Engineering Ui versity Malaysia Pahang Kuantan, Pahng, Malaysia mahuzah@ump. edu. my Abstract-In Electroencephalography (EEG) research, the analysis using is 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 resuls of this experiment shows that EEG signals can be analysed or described using ive major components of the GLCM. Kywords-EEG, specrogram iage, GLC, txture feature, eA I. NTRODUCTION he brain is an important organ in he human body. n its centre there are cells called neurons. hese neurons produce electric sinals nd can be measured using EEG. n practice, EEG sinals are in time-based format. To nalyze in requency-based, usually the sinals need to be transformed into Fourier rnsform (F). n this paper, EEG sinals were analyzed based on time-requency image processing technique or called spectrogram. he most oten technique used to analyze sinal in time-requency based is Short Time Fourier Transform (SF). he SFT is to perform a FT on the sinal, then mapping the sinal into a two-dimensional unction of requency and time. A lot of research has been conducted on time-requency based but mainly in sinal processing area [1,2]. here are studies, for example using spectrogram image in time requency to classiY heart abnormalities rom electrocrdioram (ECG) [3]. hey extracted the heiht and width of pulses and Euler number rom the spectroram image. Back-propagation NN was chosen as classiier and gives 100% accuracy in heart abnormalities classiication. Once an image is obtained, rious image processing tools, such as texture analysis, could be used. Most popular technique for the textural classiication is GLCM. he GLCM is a tabulation of rey levels requency occuring in an image. n 978-1-4244-8648-9/10/$26.00 ©2010 IEEE 426 Mohd Nasir Taib2,3, Zunairah Hj. Muraf,3, Sahrim Liai,3 2 Faculty of Electrical Engineering 3 Biomedical Research Laboratory for Human Potential University Teknologi MARA Malaysia Shah Alam, Selangor, Malaysia dr.nasir@ieee.org [4], they extract spectrogram image using normalized rey level co-occurrence matrices (NGLCM) rom ECG. his study is about detecting sleep disorder breathing in hn het. hey are using Fuzzy with result 79.29% accuracy in training nd 75. 88% accuracy in testing. Ater GLCM process done, texture features need to be extracted. he most popular teture feature extraction is proposed by Haralick [5] and Soh [6]. Haralick proposed 14 texture features to apply to the photomicroraphs of sandstones, photoraphs of land-use and satellite photoraphs of land-use while Soh popose 10 teture features for satellite photoraphs of sea ice. Principal components analysis (PCA) was used for data reduction and classiication puposes. n [7], they choose the irst three principal components which are contrast, dissimilarity and homogeneity, out of eiht GLCM texture features. As a result, the irst three components rom PCA give better accuracy in classiication than all eiht GLCM texture features. n this paper, a study of dimension reduction of GLCM textre features is presented. he feature is extracted rom EEG spectroram image. II. METHODS n this project, the EEG data were collected with scalp electrodes by using standard gold disc electrodes, with 2- channel Fpl and Fp2. his is set up using 10-20 ntenational system with 256z sampling rate. n this experiment, 28 males and 23 females agree to be volunteers as sample in this experiment. he data collections were perform at Biomedical Research and Development Laboratory for Human Potential, Faculty of Electrical Engineering, Universiti Teknologi MARA Malaysia. Fig. 1 shows the experiment low chart for this study. EEG was collected then artefacts were removed. he artefact is regarded as noise when the EEG sinals more than 1 00l V and less than -100 l V. Next, the sinal was iltered using Band pass ilter with Hamming window with 50% overlapping