AIP Conference Proceedings 2193, 050003 (2019); https://doi.org/10.1063/1.5139376 2193, 050003
© 2019 Author(s).
EEG data acquisition system 32 channels based on Raspberry Pi with relative power ratio and brain symmetry index features
Cite as: AIP Conference Proceedings 2193, 050003 (2019); https://doi.org/10.1063/1.5139376 Published Online: 10 December 2019
Henry Hendarwin, Sastra Kusuma Wijaya, Prawito Prajitno, et al.
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EEG Data Acquisition System 32 Channels based on Raspberry Pi with Relative Power Ratio and Brain
Symmetry Index Features
Henry Hendarwin
1, Sastra Kusuma Wijaya
1,a), Prawito Prajitno
1, Nurhadi Ibrahim
2, Hendra Saputra Gani
1, Wahyu Apriadi
11Department of Physics, Faculty of Mathematics and Natural Sciences, Indonesia, Kampus UI Depok, Depok, West Java, 16424, Indonesia
2Department of Medical Physiology, Faculty of Medicine, Universitas Indonesia, Jl Salemba Raya No 6, Senen, Central Jakarta 10430 Indonesia
a)Corresponding author: [email protected]
Abstract. An inhouse Electroencephalography (EEG) data acquisition system based on Raspberry Pi and Analog Front End (AFE) ADS1299 EEGFE-PDK has been developed. The system displayed a relative power ratio (RPR) and brain symmetry index (BSI) in real-time. The features of the AFE are simultaneous sampling for eight channels, 24 bits resolution, low power, and low noise of < 5 mW and < 1 µV, respectively. The system consists of 4 units AFE in daisy chain configuration. The communication between AFE and Raspberry Pi is programmable using registers of RDATA format accessed via a serial peripheral interface (SPI) and programmed using C. The data acquired were processed using MATLAB. These data were transferred using Local Area Networking (LAN) filtered based on 5th order Butterworth and processed in a personal computer (PC). The RPR was calculated using Fast Fourier Transforms (FFT) and Power Spectral Density (PSD). The BSI was calculated using Welch method. The acquired and processed data would be sent to High Definition Multiple Interface (HDMI) if needed by users. This system has been evaluated using EEG simulator (NETECH MiniSim EEG), which is generate sinusoidal electrical signal with frequency 2 Hz, 5 Hz, and voltage amplitude 30, 50μV, with error average less than 6%.
Keywords: ADS1299, BSI, Daisy Chain, EEG, Raspberry Pi, RPR
INTRODUCTION
Stroke is the disease that occurs when the blood supply to the brain is disrupted or completely reduced, so the brain tissue lacks oxygen and nutrients. Within minutes, brain cells begin to break down. Stroke is classified into two types ischemic; and hemorrhagic. Ischemic stroke is a condition that occurs when a blood vessel that supplies blood to the area of the brain is blocked by a blood clot [1].
Electroencephalography (EEG) is a noninvasive method to record the electrical activity of brain by placing electrodes on the scalp with a conductive gel or paste. EEG measures voltage fluctuation in the brain. The typical voltage amplitude of human EEG signal is about 10 to 100 µV. EEG signal was classified into 5 frequency band, there are delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz) [2, 3]. System 10-20 is the procedure for EEG electrode placement [4]. Artifact rejection is needed when EEG signal processing, because muscle activity, heartbeat, and eye movement cause noise when data record [5, 6].
Quantitative EEG (qEEG) is a procedure to transform EEG signals into domain that has relevant information to see dynamic changes in the brain [7]. The symmetry of right and left brain waves can be determined by Brain
techniques[9]. In general, the difference between stroke patients and healthy can be distinguished by the difference of asymmetry left and right brain with BSI method [10].
The human EEG signal is about 10 µV to 100 µV in amplitude. Biopotential amplifier needed to measure EEG signals. There are many types of biopotential amplifiers [11], one of them is ADS1299 [12-15]. The ADS1299 is an analog to digital converter (ADC) for bio-potential measurement with 24-bit delta-sigma high resolution, and it can measure voltage amplitude in microvolt (μV) with low noise result. ADS1299 has 8 channels of simultaneous sampling. The sampling rate of ADS1299 is about 250 to 16 ksps (kilo samples per second). Analog Front End (AFE) ADS1299 EEGFE-PDK is the board consists ADS1299 chip. Multiple devices can be configured with daisy chain or cascaded method. Raspberry Pi was used to control data acquired from ADS1299. Data from Raspberry Pi is sent to Personal Computer (PC) via Local Area Network (LAN), for signal processing in MATLAB. After data has been processed, it will be saved into European Data Format (EDF).
METHODS
Data Acquisition and Implementation
In this system, the EEG signals were acquired using Analog Front End (AFE) ADS1299 EEGFE-PDK controlled Raspberry Pi and processed in MATLAB. The communication protocol between Raspberry Pi and AFE is based on Serial Peripheral Interface (SPI) protocol. The system was developed using 32 channels based on 4 AFE with daisy chain configuration, as shown in Figure 1.
FIGURE 1. Block Diagram of Acquisition System
The dimensions of the PCB were customed design in 190.5 mm long and 100.3 mm wide using two-layer configuration. The top layer is the red line path and the bottom layer is blue line path. Each layer has two AFE modules in order to separate the analog signals and digital signals. All analog signals from head scalp are placed on the same sides. The PCB design as shown in Figure 2.
AFE
RASPBERRY PI 3 MODEL B MOSI
DAISY_IN DOUT
SCLK CS DIN
PC
AFE DAISY_IN
DOUT SCLK CS
DIN
AFE DAISY_IN
DOUT SCLK CS
DIN AFE
DOUT
SCLK CS DIN
SS SCLK MISO
LAN PORT
FIGURE 2. PCB Design Filtering
In this study use Butterworth band pass filter based on MATLAB to remove artifacts or filter out the undesired signal from outside of the range of signal band frequency. Butterworth band pass filter with fifth order, and parameter frequency range 0.5 to 50 Hz implemented as following:
l = 0.5; % lower limit h = 50; % upper limit o =5; % filter order
nyq =o*fs; % fs (frequency sampling) low = l/nyq; % calibrated lower frequency high = h/nyq; % calibrated upper frequency [b,a] = butter (o, [low, high]);
y = filter (b,a,data);
Relative Power Ratio and Brain Symmetry Index
The mean Power Spectral Density (PSD) data acquired from FFT, were applied to Relative Power Ratio (RPR) equation shown below to evaluate ratio each frequency band with all frequency band, with PL and PR are PSD of each frequency band left side and right side within as EEG frequency band.
i i
i
i i
i
PL PR RPR PL PR
(1)
Brain Symmetry Index (BSI) is the mean of the absolute value of the difference in mean hemispheric power in the EEG frequency range.
1 1
( ) ( )
1 1
BSI( )
( ) ( )
K M
ij ij
j i ij ij
R t L t t N M R t L t
(2) With i as the particular hemispheric bipolar channel pair at frequency j, Rij(t) and Lij(t) for the right and left hemisphere, N for the number of discrete-time, M for the number of channels, and K is the number of discrete frequency.
Data Validation
To determine the system’s performance of measuring EEG signal, it has been calibrated with waveform from NETECH MiniSim EEG Simulator 330. For data validation, data acquired from the created acquisition system with commercially EEG data acquisition system (NEUROSTYLE).
RESULT AND DISCUSSIONS
The Result of Design
An in-house EEG data acquisition has been built. This study uses Intel NUC5I5MYHE - Mini PC. The specifications of computer are Intel Core-i56260U, Intel graphics, 16 Gb DDR4 Ram, 500 Gb Hard Disk Drive. This uses that Mini PC because it has dimension L x W x H (11 cm x 11.54 cm x 3.6 cm) so it does not need much space.
For data acquired uses Raspberry Pi3 Model B, 4 ADS1299 Fe board with daisy chain configuration. This study uses Local Area Network (LAN) connection between Raspberry Pi and computer for data transfer and control process on Raspberry Pi. The hardware has been built as shown in Figure 3, and the Graphical User Interface (GUI) from system as shown in Figure 4.
FIGURE 3. The result of hardware design
FIGURE 4. GUI of data acquisition system
The Result of Data Validation Using Netech Minisim EEG Simulator 330
The system was calibrated using sinusoidal wave from Netech MiniSim EEG Simulator 330 at frequency 2 Hz, 5 Hz with amplitude 30 µV and 50 µV. The result was compared using NEUROSTYLE as shown in Figure 5. And Table 1.
FIGURE 5. The comparison of the sinusoidal waveform between Prototype and Neurostyle
TABLE 1. The comparison of validation Frequency
(Hz) Amplitude(µV)
Average Amplitude
(µV) Error (%)
Prototype Neurostyle Prototype Neurostyle
2 30 28 28.2 9.3 9.4
50 47.4 47.2 5.2 5,6
5 30 28.9 28.6 3.7 4.6
50 48 48.8 4 2.4
The Subject Measurement Result between Prototype with NEUROSTYLE
FIGURE 6. Data acquired comparison between an in house data acquisition system and NEUROSTYLE
Based on Figure 6, the signal obtained on the F3 channel on the in house data acquisition system and NEUROSTYLE. The signal comes from subject playing game when data recorded, in the range of 41 seconds to 48 seconds. From the comparison of two waveforms, the average amplitude at 15 µV.
FIGURE 7. RPR result comparison between in house system and NEUROSTYLE
From the results as shown in Figure 7, from each pair of bar graphs the left-side graph shows the power ratio values of the prototype and the right side shows the power ratio values of the NEUROSTYLE. The RPR calculation on subject playing game when data recorded, the average power ratio obtained from the prototype for delta, theta, alpha, beta is 11%, 4%, 7%, and 78%. The average power ratio of NEUROSTYLE for delta, theta, alpha, and beta is 11%, 4%, 7%, and 78%. Both of data acquisition system show that beta frequency band dominant when playing game.
The result comparison of BSI calculation as shown in Figure 8, with time intervals 0 to 600 seconds. The average BSI value for in house data acquisition system is 0.2 and the average BSI value for NEUROSTYLE is 0.2.
FIGURE 8. BSI result comparison between in house system and NEUROSTYLE
The Comparison of Save Data into EDF between Prototype and Neurostyle
FIGURE 9. EDF result from in house data acquisition system
FIGURE 10. EDF result from NEUROSTYLE
Based on Figure 9 and Figure 10, the signal from the subject playing game. In the acquisition system that has been made, it is able to store data in European Data Format (EDF). To read files stored in EDF format, you can use EDF readers online which can be accessed on the website https://bilalzonjy.github.io/EDFViewer/EDFViewer.html. EDF data from the acquisition system were made compared with EDF data from NEUROSTYLE. In NEUROSTYLE there is a converter for storing data from the acquisition in the form of EDF by using NED to EDF. From the results of the signal shape comparisons for channels O1, FP1, and P3, the signal forms obtained between acquisition systems made with NEUROSTYLE, show similar shapes
CONCLUSIONS
A thirty-two-channel data acquisition system based on Raspberry Pi has been built, utilizing AFE ADS1299 EEGFE-PDK in daisy chain configuration based on SPI protocol implemented with real-time processing. It was deployed in a portable prototype, displayed the acquired signals and their RPR results in real-time using C and analyzed using MATLAB. Intel NUC5I5MYHE-Mini PC used in this prototype for data processing and control process on Raspberry Pi 3 Model B. Based on RPR calculation, the dominant frequency band of data on this study is beta band because participant was playing a game when data recorded. After data has been processed, the data was saved into EDF. The created acquisition system has been calibrated with sinusoidal wave from MiniSim EEG Simulator 330 with frequency 2 Hz and 5 Hz at amplitude 30 µV and 50 µV has error 9.3%, 5.2%, 3.7%, 4%. In house data acquisition systems can save data in European Data Format (EDF).
ACKNOWLEDGEMENT
This study was supported by the Department of Education and Culture of the Republic of Indonesia through DRPM Universitas Indonesia by PDUPT 2019 with contract number NKB-1610/UN2.R31/HLP.05.00/2019.
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