HQKASAP

In the feature design aspect, these studies have converted 1D EEG data into 2D image data in advance and classified the features via the deep network. However, the majority of these studies have focused on regular data, such as the same frequency and same length of the sample data. proposed a coding method for epileptic EEG signals based on the deep network. used an in-depth learning method based on a cloud platform to propose a solution for epilepsy prevention and control. converted the frequency bands extracted from brain waves into topographical maps (2D images) through spectral power and classified the images into depth networks. Tabar and Halici converted one-dimensional (1D) brain waves into two-dimensional (2D) image data through short-time Fourier transform and accessed the deep network for classification. Some researchers have studied EEG via a deep network. In-depth learning technology can accomplish numerous tasks that are difficult to complete in the traditional methods.....