Data acquisition system of 16-channel EEG based on ATSAM3X8E ARM Cortex-M3 32-bit microcontroller and ADS1299 (original) (raw)

EEG data acquisition system 32 channels based on Raspberry Pi with relative power ratio and brain symmetry index features

THE 4TH BIOMEDICAL ENGINEERING’S RECENT PROGRESS IN BIOMATERIALS, DRUGS DEVELOPMENT, HEALTH, AND MEDICAL DEVICES: Proceedings of the International Symposium of Biomedical Engineering (ISBE) 2019

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 5 th 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%.

EEG data acquisition system 32 channels with relative power ratio based on Raspberry Pi 3

PROCEEDINGS OF THE 4TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES (ISCPMS2018)

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.

Design of EEG Signal Acquisition System Using Arduino MEGA1280 and EEGAnalyzer

MATEC Web of Conferences, 2016

This study integrates the hardware circuit design and software development to achieve a 16 channels Electroencephalogram (EEG) system for Brain Computer Interface (BCI) applications. Signals obtained should be strong enough amplitude that is usually expressed in units of millivolts and reasonably clean of noise that appears when the data acquisition process. The process of data acquisition consists of two stages are the acquisition of the original EEG signal can be done by the active electrode with an instrumentation amplifier or a preamplifier and processing the signal to get better signals with improved signal quality by removing noise using filters with IC OPAMP. The design of a preamplifier with high common-mode rejection ratio and high signal-to-noise ratio is very important. Moreover, the friction between the electrode pads and the skin as well as the design of dual power supply. Designs used single-power AC-coupled circuit, which effectively reduces the DC bias and improves the error caused by the effects of part errors. At the same time, the digital way is applied to design the adjustable amplification and filter function, which can design for different EEG frequency bands. The next step, those EEG signals received by the microcontroller through a port Analog to Digital Converter (ADC) that integrated and converted into digital signals and stored in the RAM of microcontroller which simultaneously at 16 ports in accordance with the minimal number of points of data collection on the human scalp. Implementation results have shown the series of acquisitions to work properly so that it can be displayed EEG signals via software EEGAnalyzer.

Development of electroencephalography (EEG) data acquisition system based on FPGA PYNQ

This study proposed a novel Field Programmable Gate Array (FPGA)-based 32-channel data acquisition system to acquire and process Electroencephalography (EEG) signal. The data acquisition system utilized PYNQ-Z1 board, which was equipped with a Xilinx ZYNQ XC7Z020-1CLG400C All Programmable System-on-Chip (APSoCs) that offered high performance embedded system because of the combination between the flexibility and versatility of the programmable logic (PL) and the high-speed embedded processor or programmable system (PS). As the core of the data acquisition system, the FPGA collected, processed, and stored the data based on Front-End Analog to Digital Converter (ADC) ADS1299EEG-FE. The communication protocol used in the data acquisition system was Serial Peripheral Interface (SPI) with daisy-chain configuration. For the signal processing part, a 5th-order Butterworth bandpass filter and Fast Fourier Transform (FFT) has been implemented directly on the PYNQ's Overlay. The overlay was configurable FPGA design that extend the system from the PS of the ZYNQ to the PL, enabling us to control directly the hardware platform using Python running in the PS. The mean accuracy error obtained from validation result of the developed system was 1.34% and the Total Harmonic Distortion (THD) performance criterion resulting in 0.0091%, both of them validated with NETECH MiniSIM EEG Simulator 330. The comparison between the developed system and Neurostyle NS-EEG-D1 System acquiring the same EEG data shows correlation parameter gradient of 0.9818, y-intercept with-0.1803, and R squared of 0.9742 based on the least square analysis. The parameter above indicated that the developed system was adequate enough, if not on a par, with the commercialized, medical grade EEG data acquisition system Neurostyle NS-EEG-D1 as the system assured and maintained accuracy with higher sampling frequency.

Development of a Modular Board for EEG Signal Acquisition

Sensors, 2018

The increased popularity of brain-computer interfaces (BCIs) has created a new demand for miniaturized and low-cost electroencephalogram (EEG) acquisition devices for entertainment, rehabilitation, and scientific needs. The lack of scientific analysis for such system design, modularity, and unified validation tends to suppress progress in this field and limit supply for new low-cost device availability. To eliminate this problem, this paper presents the design and evaluation of a compact, modular, battery powered, conventional EEG signal acquisition board based on an ADS1298 analog front-end chip. The introduction of this novel, vertically stackable board allows the EEG scaling problem to be solved by effectively reconfiguring hardware for small or more demanding applications. The ability to capture 16 to 64 EEG channels at sample rates from 250 Hz to 1000 Hz and to transfer raw EEG signal over a Bluetooth or Wi-Fi interface was implemented. Furthermore, simple but effective assessment techniques were used for system evaluation. While conducted tests confirm the validity of the system against official datasheet specifications and for real-world applications, the proposed quality verification methods can be further employed for analyzing other similar EEG devices in the future. With 6.59 microvolts peak-to-peak input referred noise and a −97 dB common mode rejection ratio in 0-70 Hz band, the proposed design can be qualified as a low-cost precision cEEG research device.

Eeg Signal Acquisition Using Embedded Systems

2016

The purpose of this paper is to develop EEG signal acquisition using embedded systems. This system has been designed that recorded biological signal conditioning is a successive analog and digital transformation. These transformations are necessary to provide signals for efficacious signal processing and pattern recognition methods. An important objective of this paper is to describe the improvements of the EEG signal acquisition systems using efficient signal conditioning proceedings which gives increased amplification factor and SNR value.

BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool System

Stable and accurate electroencephalogram (EEG) signal acquisition is fundamental in non-invasive brain-computer interface (BCI) technology. Commonly used EEG acquisition system’s hardware and software are usually closed-source. Its inability to flexible expansion and secondary development is a major obstacle to real-time BCI research. This paper presents the Beijing University of Posts and Telecommunications EEG Acquisition Tool System named BEATS. It implements a comprehensive system from hardware to software, composed of the analog front-end, microprocessor, and software platform. BEATS is capable of collecting 32-channel EEG signals at a guaranteed sampling rate of 4k Hz with wireless transmission. Compared to state-of-the-art systems used in many EEG fields, it displays a better sampling rate. Using techniques including direct memory access, first in first out, and timer, the precision and stability of the acquisition are ensured at the microsecond level. An evaluation is conduc...

A Wireless EEG Acquisition Platform based on Embedded Systems

Proceedings of the International Conference on Biomedical Electronics and Devices, 2013

This paper proposes a wireless EEG acquisition platform based on Open Multimedia Architecture Platform (OMAP) embedded system. A high-impedance active dry electrode was tested for improving the scalpelectrode interface. It was used the sigma-delta ADS1298 analog-to-digital converter, and developed a "kernelspace" character driver to manage the communications between the converter unit and the OMAP's ARM core. The acquired EEG signal data is processed by a "userspace" application, which accesses the driver's memory, saves the data to a SD-card and transmits them through a wireless TCP/IP-socket to a PC. The electrodes were tested through the alpha wave replacement phenomenon. The experimental results presented the expected alpha rhythm (8-13 Hz) reactiveness to the eyes opening task. The driver spends about 725 μs to acquire and store the data samples. The application takes about 244 μs to get the data from the driver and 1.4 ms to save it in the SD-card. A WiFi throughput of 12.8Mbps was measured which results in a transmission time of 5 ms for 512 kb of data. The embedded system consumes about 200 mAh when wireless off and 400 mAh when it is on. The system exhibits a reliable performance to record EEG signals and transmit them wirelessly. Besides the microcontroller-based architectures, the proposed platform demonstrates that powerful ARM processors running embedded operating systems can be programmed with real-time constrains at the kernel level in order to control hardware, while maintaining their parallel processing abilities in high level software applications.

EEG Data Acquisition System and Analysis of EEG Signals

IEEE Conference, INCET 2021, 2021

The main objective of this manuscript is to perform an EEG Data Acquisition, Analysis and Present the EEG signals. It consists of innovative pre-processing method by utilizing amplifier with high impedance and high CMRR. It also consists of Analog filters to remove the noise and to prevent from Aliasing. Analysis is done through FFT algorithm using MATLAB. Findings of the frequency of the signals corresponding to peak magnitude are put forward as the Presentation of EEG signals. As the biomedical signals are having low frequency and low amplitude, the pre-processing, filtering, conversion are done with accuracy, requires adequate sampling rate, processing speed, gain, power consumption and size.

The system of electric brain activity acquisition from EEG equipment for Linux OS

Annales Umcs, Informatica, 2008

The idea of electric brain activity acquisition for LINUX OS is presented. So far the only available software has been that for MS-Windows. The general concept of the modular data acquisition system as independent of its particular parts as possible and based on the LINUX pipe system will be discussed to some extent. Adapting the hardware to open source technology will allow us to design new methods of brain electrical dynamic analysis.