Window-based Time-Frequency Methods for Analyzing Epileptic EEG Signals

Abstract

Epilepsy is a chronic non-communicable disease caused by abnormal firing activity of brain neurons in all age groups. This research studies two time-frequency domain analysis methods of EEG signals, short-time Fourier transform and continuous wavelet transform, using these two methods to analyze one piece of epilepsy EEG signals. The window size will affect the time resolution and frequency resolution for the short-time Fourier transform. The larger the window size, the lower the time resolution and the higher the frequency resolution, and vice versa. Therefore, it is vital to choose the most suitable window size. The best window size is 0.4s through experiments; for continuous wavelet transform, is a parameter that controls the scale of the Gaussian kernel, and $\omega$ is the frequency of Morlet. The rule is obtained through experiments; when the results of $\sigma \times \omega$ are between 2 and 4, the analysis results can simultaneously exhibit higher time, frequency resolution, and more details. No matter what the values of $\sigma$ and $\omega$ are, as long as the product of the two is the same, the analysis results are the same. Finally, this study obtained the seizures trend. The trend of epileptic seizures mainly started from the right side of the brain, moved to the left side, then to the forehead, and finally to the occipital brain region.