Epileptic seizure detection using a non-invasive brain-machine interfacing system

Jeevan K. M.; M. Ramesha; Chethana S.

Author(s)

Jeevan K. M. GITAM School of Technology,Bangalore,India

M. Ramesha GITAM School of Technology,Bangalore,India

Chethana S. GITAM School of Technology,Bangalore,India

Abstract

This paper proposes a Brain-Machine Interface (BMI) for aiding epileptic seizure patients by monitoring their brain waves through electroencephalogram (EEG) recordings. EEG signals are highly prone to artifacts and noise from blinking eyes, muscle movements, and glossokinetic artifacts. Hence filtering must be done for the EEG brain wave obtained. The EEG signal received can be classified as a normal person's or an epileptic patient's based on the features extracted from these two categories. If an epileptic seizure will be predicted, then a distress signal is sent automatically to a centralized monitoring system through a GSM module. The patient's current location is acquired using a GPS module and transmitted along with the message to get immediate attention. From experimentation, it is found that the proposed methodology provides a highly accurate detection capability with high sensitivity, which is superior to other methods. The implementation has been carried out on the Spartan6 FPGA kit, using Xilinx ISE Design Suite 14.2, programmed in Verilog HDL. The GPS used is Neo-6M, and GSM is SIM900A. The embedded board STM32F0 discovery kit has also been used.

Publication Details

Page(s) 99-105

DOI DOI not available

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 17, Issue: 1, Year: 2022

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Keywords

brain-machine interface, Feature Classification, Epilepsy, feature extraction

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