(untitled) (Aintelia Science Notes, 2022)
Electroencephalogram (EEG) signals are low-amplitude electrical signals that measure the electrical activity between electrodes from the scalp and neurons in the brain. Successful studies have been carried out in many different areas for the detection of many neurological diseases, especially epilepsy, using EEG signals. In this study is aimed to compare different spectral analysis methods on EEG data. For this purpose, three different feature vectors were created by calculating power spectrum densities between 1-49 Hz using three different spectral analysis methods: Periodogram, Welch, and Multitaper. The performances of the three spectral analysis methods were compared by classifying them with the Support Vector Machine (SVM) algorithm using the created feature vectors. The accuracy rate of the Periodogram and SVM model was 92.30 %, the accuracy rate of the Welch and SVM model was 96.16 %, the accuracy rate of the Multitaper and SVM model was 94.48 %. The model with the highest performance is the classification model that effectively combines the Welch method and the SVM algorithm.