EEG Signal Acquisition System Research Articles

Nanocomposite-Based Electrode Structures for EEG Signal Acquisition

Objective: To fabricate a lightweight, breathable, comfortable, and able to contour to the curvilinear body shape, electrodes built on a flexible substrate are a significant growth in wearable health monitoring. This research aims to create a GNP/FE electrode-based EEG signal acquisition system that is both efficient and inexpensive. Methodology: Three distinct electrode concentrations were developed for EEG signal acquisition, three distinct electrode concentrations (1.5:1.5, 2:1, and 3:0). The high strength-to-weight ratio to form the tribofilm in the fabrication of the electrode will provide good efficiency. The EEG signal is first subjected to a wavelet transform, which serves as a preliminary analysis. The use of biopotential signals in wearable systems as biofeedback or control commands is expected to substantially impact point-of-care health monitoring systems, rehabilitation devices, human–computer/machine interfaces (HCI/HMI), and brain–computer interfaces (BCIs). The graphene oxide (GO), glycerol (GL), and polyvinyl alcohol (PVA) GO/GL/PVA plastic electrodes were measured and compared to that of a commercially available electrode using the biopic equipment. The GO/GL/PVA plastic electrode was able to detect EEG signals satisfactorily after being used for two months, demonstrating good conductivity and lower noise than the commercial electrode. The GO/GL/PVA nanocomposite mixture was put into the electrode mold as soon as it was ready and then rapidly chilled. Results: The quality of an acquired EEG signal could be measured in several ways including by its error percentage, correlation coefficient, and signal-to-noise ratio (SNR). The fabricated electrode yield detection ranged from 0.81 kPa−1 % to 34.90 kPa−1%. The performance was estimated up to the response of 54 ms. Linear heating at the rate of 40 °C per minute was implemented on the sample ranges from 0 °C to 240 °C. During the sample electrode testing in EEG signal analysis, it obtained low impedance with a good quality of signal acquisition when compared to a conventional wet type of electrode. Conclusions: A large database was frequently built from all of the simulated signals in MATLAB code. Through the experiment, all of the required data were collected, checked against all other signals, and proven that they were accurate representations of the intended database. Evidence suggests that graphene nanoplatelets (GNP) hematite (FE2O3) polyvinylidene fluoride (PVDF) GNP/FE2O3@PVDF electrodes with a 3:0 concentration yielded the best outcomes.

Portable Multi Channel EEG Signal Acquisition System

This study introduces a portable multi-channel EEG signal acquisition system. The system is mainly composed of EEG electrode connector, signal conditioning circuit, EEG acquisition part, main control MCU and power supply part. The low-power EEG acquisition front-end ADS1299 and STM32 are used to form the signal acquisition and data communication part. The collected EEG signal can be transmitted to the PC for real-time display. After relevant tests, the system has small volume, low power consumption, high signal-to-noise ratio, and meets the requirements of portable wearable medical devices.

Development of a Hardware Circuit for Real-Time Acquisition of Brain Activity Using NI myDAQ

Electroencephalography (EEG) is the recording of electrical activity of the brain. The 10–20 system is the standard electrode location method used to acquire EEG data, which uses 21 electrodes to record the electrical activity of the brain. Patient preparation and correct electrode placement are important to obtain reliable outputs. The current 10–20 system consumes greater time for patient preparation and also causes discomfort due to a higher number of electrodes being used or wearing an uncomfortable cap. This paper focuses on reducing the number of electrodes, thus reducing patient discomfort as well as preparation time. Advancement in the field of hardware and software processing has led to the utilization of brain waves for communication between human and the computer. This work deals with EEG-based Brain–Machine Interface (BMI) intended for designing a portable single-channel EEG signal acquisition system. EEG signal was acquired using the data acquisition module [National Instruments (NI) myDAQ] and the signal was viewed in the NI Laboratory Virtual Instrument Engineering Workbench (LabVIEW) environment. It was observed that the peak-to-peak amplitude of alpha, beta and theta waves changes in accordance with the activity the subjects performed. Thus, the developed instrument was tested on 10 different subjects to acquire the alpha, beta and theta waves by performing different activities. From the results, it can be concluded that the developed system can be used for studying a person’s brain waves (alpha, beta and theta) based on the activity performed by the subject with a limited number of electrodes.

Design and Verification of a Dry Sensor-Based Multi-Channel Digital Active Circuit for Human Brain Electroencephalography Signal Acquisition Systems.

A brain–computer interface (BCI) is a type of interface/communication system that can help users interact with their environments. Electroencephalography (EEG) has become the most common application of BCIs and provides a way for disabled individuals to communicate. While wet sensors are the most commonly used sensors for traditional EEG measurements, they require considerable preparation time, including the time needed to prepare the skin and to use the conductive gel. Additionally, the conductive gel dries over time, leading to degraded performance. Furthermore, requiring patients to wear wet sensors to record EEG signals is considered highly inconvenient. Here, we report a wireless 8-channel digital active-circuit EEG signal acquisition system that uses dry sensors. Active-circuit systems for EEG measurement allow people to engage in daily life while using these systems, and the advantages of these systems can be further improved by utilizing dry sensors. Moreover, the use of dry sensors can help both disabled and healthy people enjoy the convenience of BCIs in daily life. To verify the reliability of the proposed system, we designed three experiments in which we evaluated eye blinking and teeth gritting, measured alpha waves, and recorded event-related potentials (ERPs) to compare our developed system with a standard Neuroscan EEG system.

Portable brain-computer interface based on novel convolutional neural network

Electroencephalography (EEG) is a powerful, noninvasive tool that provides a high temporal resolution to directly reflect brain activities. Conventional electrodes require skin preparation and the use of conductive gels, while subjects must wear uncomfortable EEG hats. These procedures usually create a challenge for subjects. In the present study, we propose a portable EEG signal acquisition system. This study consists of two main parts: 1) A novel, portable dry-electrode and wireless brain–computer interface is designed. The EEG signal acquisition board is based on 24 bit, analog-to-digital converters chip and wireless microprocessor unit. The wireless portable brain computer interface device acquires an EEG signal comfortably, and the EEG signals are transmitted to a personal computer via Bluetooth. 2) A convolutional neural network (CNN) classification algorithm is implemented to classify the motor imagery (MI) experiment using novel feature 3-dimension input. The time dimension was reshaped to represent the first and second dimension, and the frequency band was used as the third dimension. Specifically, frequency domain representations of EEG signals obtained via wavelet package decomposition (WPD) are obtained to train CNN. The classification performance in terms of the value of kappa is 0.564 for the proposed method. The t-test results show that the performance improvement of CNN over other selected state-of-the-art methods is statistically significant. Our results show that the proposed design is reliable in measuring EEG signals, and the 3D CNN provides better classification performance than other method for MI experiments.

A Low-Cost Portable Real-Time EEG Signal Acquisition System Based on DSP

EEG apparatus are expensive and bulky. Their real-time performance is weak, and EEG signals are easy to be distorted. In this paper, a low-cost portable EEG signal acquisition system based on DSP is developed. By a noninvasive method with bipolar leads, weak EEG signals are induced to the pre-processing circuits, where they will undergo multi-level amplifying and filtering. Then, the analog signals are converted into digital signals using ADS8320. These digitized signals are filtered in DSP (TMS320VC5509) so that the power interference and physiological artifacts are removed with LMS (Last Mean Square) algorithm and ICA (Independent Component Analysis) algorithm. Experimental results demonstrate that the system can acquire weak EEG signals in real time, display and save the processing results. The acquisition system has the advantages of usableness and portability, and helpful to the popularity of communitybased and family-based EEG diagnostic equipments.

Design of Single Channel Portable EEG Signal Acquisition System for Brain Computer Interface Application

Design of Single Channel Portable EEG Signal Acquisition System for Brain Computer Interface Application

A New Design of Portable EEG Signal Acquisition System

Most of the electroencephalograph (EEG) acquisition systems use group of high precision electrical signal amplifier to record and analysis. But the huge input circuit and testing time is difficult to application for portable devices. In this paper, a new design of portable EEG signal acquisition system is designed. This paper presents an effective chip (ADS1299) implementation of an eight-channel EEG signal acquisition. Compared with the existing system, this design greatly simplifies the front-end circuits and improves the common mode rejection ratio (CMRR). The system has the features of high integration density, good flexibility and practicability. It is in line with the development trend of modern EEG, and meets the new requirements of modern EEG.

Implementation of Portable Multi-Channel EEG and Head Motion Signal Acquisition System

In this paper, we suggest a portable multi-channel electroencephalography (EEG) and head motion signal acquisition system, which enables people to measure brainwave signals anytime and anywhere. Many researchers have recently focused on Brain-Computer Interfaces (BCI). Existing studies on portable multi-channel EEG technologies have suggested EEG technologies that can be applied to the human forehead and used indoors, but failed to present a technology capable of measuring brainwaves originating from various brain areas in both indoor and outdoor environments. Our proposed system has a multi-channel EEG function to measure brainwaves and head motion originating from more diverse areas of the brain than the existing systems. The system suggested in this paper also has a Bluetooth interworking capability to provide a wireless connection to portable devices such as smart phones. The mechanism is to amplify very small brainwave signals, remove noise, perform AD conversion, and finally measure brainwaves and transmit the data to smart phones or other portable devices. Indoor and outdoor tests on the device proved the validity of the portable multi-channel EEG signal acquisition system.