Design of EEG data acquisition system based on Raspberry Pi 3 for acute ischemic stroke identification

Rizki Arif, Sastra Kusuma Wijaya, Prawito, Hendra Saputra Gani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

This study demonstrates the feasibility of identifying and quantifying pathological changes in brain electrical activity with a portable eight-channel data acquisition system based on Raspberry Pi 3 and MATLAB-based Graphical User Interface (GUI) to perform analyses on Electroencephalogram (EEG) signal including Fast Fourier Transform (FFT), Power Spectral Density (PSD), Relative Power Ratio (RPR), and Brain Symmetry Index (BSI). These parameters are important for analyzing various electrical brain activities including confirmation of acute ischemic stroke and EEG biofeedback analysis for stroke rehabilitation. The data acquisition system is using Raspberry Pi 3 and Front-End Analog to Digital Converter (ADC) ADS1299EEG-FE to stream the data that will be processed and displayed in the MATLAB-based GUI. The accuracy error obtained from validation result of the developed system is 2.18% and the Total Harmonic Distortion (THD) performance criterion resulting in 1.58% for square wave and 1.73% for sine wave. The system will be used in another study to identify acute ischemic stroke and as the rehabilitation tool, especially the post-stroke motor function.

Original languageEnglish
Title of host publication2018 International Conference on Signals and Systems, ICSigSys 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages271-275
Number of pages5
ISBN (Electronic)9781538656891
DOIs
Publication statusPublished - 4 Jun 2018
Event2nd International Conference on Signals and Systems, ICSigSys 2018 - Bali, Indonesia
Duration: 1 May 20183 May 2018

Publication series

Name2018 International Conference on Signals and Systems, ICSigSys 2018 - Proceedings

Conference

Conference2nd International Conference on Signals and Systems, ICSigSys 2018
CountryIndonesia
CityBali
Period1/05/183/05/18

Keywords

  • ADS1299EEG-FE
  • MATLAB
  • Raspberry Pi 3
  • electroencephalography
  • graphical user interface

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