@inproceedings{978fb51a9e24447285317ac9f8af5314,
title = "EEG data acquisition system 32 channels with relative power ratio based on Raspberry Pi 3",
abstract = "A prototype of Electroencephalography (EEG) data acquisition system based on Raspberry Pi 3 model B and ADS1299EEGFE-PDK (Texas Instruments) has been developed. This system is compact, cost effective, and has high levels of accuracy and portability. This system has feature of relative power ratio (RPR) for further processing purposes. The analog front end (AFE) board consists of ADS 1299 chip with features of simultaneous eight-channels ADC, 24 bits resolution, low power, low noise. The data acquisition system was configured with 4 units of AFE board using Daisy-Chain method via Serial Peripheral Interface (SPI) and processed based on Raspberry. The data command of AFE were set using RDATA format. The data acquisition system using C for data acquired and MATLAB for signal processing. The RPR were calculated in real-time using FFT. The acquired and processed data would be sent to HDMI and to a Personal Computer (PC) if needed by users. The performance of this system was evaluated using EEG simulator (NETECH Mini-Sim EEG) that generate sinusoidal electrical signal with frequency 2 Hz, 5 Hz, and also voltage amplitude of 100 μV, 500 μV.",
keywords = "ADS-1299, Daisy-Chain, EEG, Raspberry Pi, RPR",
author = "H. Hendarwin and P. Prajitno and Wijaya, {S. K.}",
note = "Publisher Copyright: {\textcopyright} 2019 Author(s).; 4th International Symposium on Current Progress in Mathematics and Sciences 2018, ISCPMS 2018 ; Conference date: 30-10-2018 Through 31-10-2018",
year = "2019",
month = nov,
day = "4",
doi = "10.1063/1.5132444",
language = "English",
series = "AIP Conference Proceedings",
publisher = "American Institute of Physics Inc.",
editor = "Terry Mart and Djoko Triyono and Anggraningrum, {Ivandini T.}",
booktitle = "Proceedings of the 4th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2018",
address = "United States",
}