SEARCH RESULT

EEG signals are commonly used data sources in BCI applications. For this reason, recent studies to analyze the EEG signals in the most accurate way are increasing rapidly. When features are extracted from EEG signals, the use of methods sensitive to local variations is of great importance for correct classification of the signals. In this study, 1D-local binary pattern (LBP) method which is sensitive to local changes was applied to motor imager/movement EEG signals and the obtained features were classified with the k-NN and SVM classifiers. Accordingly, in the case of using the k-NN method, the lowest 99.98%, and highest 100% classification accuracy was obtained.

International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES

Funda Kutlu Onay Cemal Kose