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.

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