FPGA-based ANN Design for Detecting Epileptic Seizure in EEG Signal

Abstract

This study aims to represent an FPGA (Field
 Programmable Gate Array) design of Artificial Neural Network (ANN) for
 Electroencephalography (EEG) signal processing in order to detect epileptic
 seizure. For analyzing brain’s electrical activity, feedforward ANN model is
 used for classification of EEG signals. The designed ANN output layer makes a
 decision whether the person has epilepsy or not. In the proposed system, the
 ANN model is programmed and simulated on Xilinx ISE editor via computer and
 then, EEG signal data are transferred to FPGA-based ANN emulator core. The Core
 is trained on data which are patient’s data and healthy person’s data. After
 training, test data is loaded to ANN Emulator Core to detect any epileptic seizure
 of person’s EEG signal. The main advantage of FPGA in the system is to improve
 speed and accuracy for epileptic seizure detection.