scholarly journals Development of a Modular Board for EEG Signal Acquisition

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2140 ◽  
Author(s):  
Tomas Uktveris ◽  
Vacius Jusas
Keyword(s):  
Signal Acquisition ◽  
Eeg Signal

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

Micromachines ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 720
Author(s):  
Chin-Teng Lin ◽  
Chi-Hsien Liu ◽  
Po-Sheng Wang ◽  
Jung-Tai King ◽  
Lun-De Liao
Keyword(s):  
Daily Life ◽  
Event Related Potentials ◽  
Computer Interface ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Preparation Time ◽  
Active Circuit ◽  
Related Potentials ◽  
Common Application

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.

A portable real-time EEG signal acquisition and tele-medicine system

2020 ◽  
pp. 1-11
Author(s):  
Qiang Zhang ◽  
Peng Wang ◽  
Shanshan Li ◽  
Yonghao Jing
Keyword(s):  
Real Time ◽  
Sql Server ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Eeg Data ◽  
Sql Server Database ◽  
High Power Consumption ◽  
Machine Interface ◽  
Sd Card

Since electroencephalogram (EEG) signals contain a variety of physiological and pathological information, they are widely used in medical diagnosis, brain machine interface and other fields. The existing EEG apparatus are not perfect due to big size, high power consumption and using cables to transmit data. In this paper, a portable real-time EEG signal acquisition and tele-medicine system is developed in order to improve performance of EEG apparatus. The weak EEG signals are induced to the pre-processing circuits via a noninvasive method with bipolar leads. After multi-level amplifying and filtering, these signals are transmitted to DSP (TMS320C5509) to conduct digital filtering. Then, the EEG signals are displayed on the LCD screen and stored in the SD card so that they can be uploaded to the server through the internet. The server employs SQL Server database to manage patients’ information and to store data in disk. Doctors can download, look up and analyze patients’ EEG data using the doctor client. Experimental results demonstrate that the system can acquire weak EEG signals in real time, display the processed results, save data and carry out tele-medicine. The system can meet the requirement of the EEG signals’ quality, and are easy to use and carry.

scholarly journals Embedded EEG Signal Acquisition Systems

2014 ◽  
Vol 12 ◽  
pp. 141-147 ◽  
Author(s):  
Lajos Losonczi ◽  
László F. Márton ◽  
Tihamér S. Brassai ◽  
Loránd Farkas
Keyword(s):  
Signal Acquisition ◽  
Eeg Signal

scholarly journals Hypovigilance Detection for UCAV Operators Based on a Hidden Markov Model

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yerim Choi ◽  
Namyeon Kwon ◽  
Sungjun Lee ◽  
Yongwook Shin ◽  
Chuh Yeop Ryo ◽  
...  
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Military Technology ◽  
Real World Data ◽  
Accident Rate ◽  
Detection Model ◽  
Aerial Vehicles ◽  
Unmanned Combat Aerial Vehicles

With the advance of military technology, the number of unmanned combat aerial vehicles (UCAVs) has rapidly increased. However, it has been reported that the accident rate of UCAVs is much higher than that of manned combat aerial vehicles. One of the main reasons for the high accident rate of UCAVs is the hypovigilance problem which refers to the decrease in vigilance levels of UCAV operators while maneuvering. In this paper, we propose hypovigilance detection models for UCAV operators based on EEG signal to minimize the number of occurrences of hypovigilance. To enable detection, we have applied hidden Markov models (HMMs), two of which are used to indicate the operators’ dual states, normal vigilance and hypovigilance, and, for each operator, the HMMs are trained as a detection model. To evaluate the efficacy and effectiveness of the proposed models, we conducted two experiments on the real-world data obtained by using EEG-signal acquisition devices, and they yielded satisfactory results. By utilizing the proposed detection models, the problem of hypovigilance of UCAV operators and the problem of high accident rate of UCAVs can be addressed.

A Portable EEG Signal Acquisition System

2012 ◽  
Vol 14 (1) ◽  
pp. 222-226
Author(s):  
Dong-Gyu Kim ◽  
Yong-Wan Roh ◽  
Kwang-Seok Hong
Keyword(s):  
Acquisition System ◽  
Signal Acquisition ◽  
Eeg Signal

scholarly journals Improvement of EEG Signal Acquisition: An Electrical Aspect for State of the Art of Front End

2010 ◽  
Vol 2010 ◽  
pp. 1-7 ◽  
Author(s):  
Ali Bulent Usakli
Keyword(s):  
State Of The Art ◽  
Signal Acquisition ◽  
Electronic Noise ◽  
Eeg Signal ◽  
Signal Conversion ◽  
Electronic Circuitry ◽  
Front End ◽  
Recording Session ◽  
Reduction Methods ◽  
Electroencephalogram Eeg

The aim of this study is to present some practical state-of-the-art considerations in acquiring satisfactory signals for electroencephalographic signal acquisition. These considerations are important for users and system designers. Especially choosing correct electrode and design strategy of the initial electronic circuitry front end plays an important role in improving the system's measurement performance. Considering the pitfalls in the design of biopotential measurement system and recording session conditions creates better accuracy. In electroencephalogram (EEG) recording electrodes, system electronics including filtering, amplifying, signal conversion, data storing, and environmental conditions affect the recording performance. In this paper, EEG electrode principles and main points of electronic noise reduction methods in EEG signal acquisition front end are discussed, and some suggestions for improving signal acquisition are presented.

scholarly journals Application of music in relief of driving fatigue based on EEG signals

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Qingjun Wang ◽  
Zhendong Mu
Keyword(s):  
Traffic Accidents ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Driving Time ◽  
Mode Decomposition ◽  
Depth Analysis ◽  
Driving Fatigue ◽  
Highly Correlated ◽  
Fatigue Driving

AbstractIn order to solve the problem of traffic accidents caused by fatigue driving, the research of EEG signals is particularly important, which can timely and accurately determine the fatigue state and take corresponding measures. Effective fatigue improvement measures are an important research topic in the current scientific field. The purpose of this article is to use EEG signals to analyze fatigue driving and prevent the dangers and injuries caused by fatigue driving. We designed the electroencephalogram (EEG) signal acquisition model to collect the EEG signal of the experimenter, and then removed the noise through the algorithm of Variational Mode Decomposition (VMD) and independent component analysis (ICA). On the basis of in-depth analysis and full understanding, we learned about the EEG signal of the driver at different driving times and different landscape roads, and provided some references for the study of music in relieving driving fatigue. The results of the study show that in the presence of music, the driver can keep the EEG signal active for more than 2 h, while in the absence of music, the driver’s EEG signal is active for about 1.5 h. Under different road conditions, the driver’s EEG signal activity is not consistent. The β wave and (α + θ)/β ratio of the driver in mountainous roads and grassland road landscape environments are highly correlated with driving time, and β wave is negatively correlated with driving time, and (α + θ)/β is positively correlated with driving time. In addition, the accumulation of changes in the two indicators is also strongly correlated with driving time.

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