TY - GEN
T1 - Design of EEG data acquisition system based on Raspberry Pi 3 for acute ischemic stroke identification
AU - Arif, Rizki
AU - Wijaya, Sastra Kusuma
AU - Prawito, null
AU - Gani, Hendra Saputra
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/4
Y1 - 2018/6/4
N2 - 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.
AB - 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.
KW - ADS1299EEG-FE
KW - MATLAB
KW - Raspberry Pi 3
KW - electroencephalography
KW - graphical user interface
UR - http://www.scopus.com/inward/record.url?scp=85049328326&partnerID=8YFLogxK
U2 - 10.1109/ICSIGSYS.2018.8372771
DO - 10.1109/ICSIGSYS.2018.8372771
M3 - Conference contribution
AN - SCOPUS:85049328326
T3 - 2018 International Conference on Signals and Systems, ICSigSys 2018 - Proceedings
SP - 271
EP - 275
BT - 2018 International Conference on Signals and Systems, ICSigSys 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Signals and Systems, ICSigSys 2018
Y2 - 1 May 2018 through 3 May 2018
ER -