Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics
Setiawan Beu, Donny
Unknown Affiliation
Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Modification of grey-level co-occurrence matrix for epileptic electroencephalogram signal classification Setiawan Beu, Donny; Rizal, Achmad; Ziani, Said; Triwiyanto, Triwiyanto
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9105

Abstract

Texture analysis is a fundamental approach in image processing for identifying specific patterns or structures. One widely used method is the grey-level co-occurrence matrix (GLCM), which computes the frequency of pixel value pairs at certain distances and angles. This study adapts the GLCM method for 1D electroencephalogram (EEG) signal analysis, focusing on extracting features such as contrast, energy, homogeneity, correlation, and entropy. EEG signals are normalized to the range 0–255, and the extracted features are classified using a support vector machine (SVM). Experimental results show that combining features across multiple distances (d=1 to 20) achieves classification accuracy of 78.8% for five classes (Z/O/N/F/S), 94.0% for three classes (O/F/S), and 94.3% for another three-class group (Z/N/F). The method shows strong performance for short to medium distances and fewer class combinations. However, performance declines when dealing with more complex class sets and longer distances, where texture features become less effective. The drop in accuracy for Z/O/N/F/S beyond d=5 underscores the challenges of maintaining feature robustness at extended distances. Despite this, GLCM remains a promising approach for EEG signal classification. Future work should focus on optimizing distance parameters and feature combinations to further enhance classification performance.
Co-Authors Achmad Rizal Ziani, Said