An Increase of the Central Frequency of Alpha Brain Wave Effected by Soy Peptide in Open Eyes Condition based EEG Signals

2019 ◽  
Author(s):  
Arjon Turnip ◽  
Dwi Esti Kusumandari ◽  
Dessy Novita ◽  
Narendra Duhita
Keyword(s):  
Eeg Signals ◽  
Central Frequency ◽  
Brain Wave

scholarly journals Design of a 32-Channel EEG System for Brain Control Interface Applications

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Ching-Sung Wang
Keyword(s):  
System Design ◽  
Frequency Band ◽  
Analog Circuit ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Eeg Signals ◽  
Filter Function ◽  
Brain Wave ◽  
Dc Bias ◽  
Measurement Verification

This study integrates the hardware circuit design and the development support of the software interface to achieve a 32-channel EEG system for BCI applications. Since the EEG signals of human bodies are generally very weak, in addition to preventing noise interference, it also requires avoiding the waveform distortion as well as waveform offset and so on; therefore, 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 will generate DC bias which affects the measurement signals. For this reason, this study specially designs an improved 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. For the analog circuit, a frequency band will be taken out through the filtering circuit and then the digital filtering design will be used to adjust the extracted frequency band for the target frequency band, combining with MATLAB to design man-machine interface for displaying brain wave. Finally the measured signals are compared to the traditional 32-channel EEG signals. In addition to meeting the IFCN standards, the system design also conducted measurement verification in the standard EEG isolation room in order to demonstrate the accuracy and reliability of this system design.

scholarly journals Study on EEG Feature Extraction under LED Color Exposure to Enhance the Concentration

2011 ◽  
Vol 2-3 ◽  
pp. 261-265 ◽  
Author(s):  
Jung Eun Lim ◽  
Bo Hyeok Seo ◽  
Sun Hyun Kim ◽  
Soon Yong Chun
Keyword(s):  
Feature Extraction ◽  
Task Performance ◽  
Eeg Signals ◽  
Biometric Data ◽  
Brain Wave ◽  
Brain Waves ◽  
Frequency Domains ◽  
The Brain

Since brain waves are expressed in a variety of frequency domains, they were used to analyze a correlation between colors and concentration. In this study, the brain wave reacting when exposed to colors was defined as a color brain wave (CBW). Also the colors on the table were changed during task performance to see colors’ influence on improving concentration and then the brave waves were measured for analysis on and comparison with the findings from the task performance. Based on the biometric data experiment conducted, it was confirmed that the findings during the task performance and those from EEG signals have a correlation and that human’s concentration is thus affected by changes of colors.

Machine learning approaches for binary classification using brain signals

2020 ◽  
Author(s):  
Tejas Wadiwala ◽  
Vikas Trikha ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Approaches ◽  
Eeg Signals ◽  
Brain Wave ◽  
Brain Signals ◽  
Learning Practices ◽  
The Brain

<p><b>This paper attempts to perform a comparative analysis of brain signals dataset using various machine learning classifiers such as random forest, gradient boosting, support vector machine, extra trees classifier. The comparative analysis is accomplished based on the performance parameters such as accuracy, area under the ROC curve (AUC), specificity, recall, and precision. The key focus of this paper is to exercise the machine learning practices over an Electroencephalogram (EEG) signals dataset provided by Rochester Institute of Technology and to provide meaningful results using the same. EEG signals are usually captivated to diagnose the problems related to the electrical activities of the brain as it tracks and records brain wave patterns to produce a definitive report on seizure activities of the brain. While exercising machine learning practices, various data preprocessing techniques were implemented to attain cleansed and organized data to predict better results and higher accuracy. Section II gives a comprehensive presurvey of existing work performed so far on the same; furthermore, section III sheds light on the dataset used for this research.</b></p>

scholarly journals Machine learning approaches for binary classification using brain signals

2020 ◽  
Author(s):  
Tejas Wadiwala ◽  
Vikas Trikha ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Approaches ◽  
Eeg Signals ◽  
Brain Wave ◽  
Brain Signals ◽  
Learning Practices ◽  
The Brain

<p><b>This paper attempts to perform a comparative analysis of brain signals dataset using various machine learning classifiers such as random forest, gradient boosting, support vector machine, extra trees classifier. The comparative analysis is accomplished based on the performance parameters such as accuracy, area under the ROC curve (AUC), specificity, recall, and precision. The key focus of this paper is to exercise the machine learning practices over an Electroencephalogram (EEG) signals dataset provided by Rochester Institute of Technology and to provide meaningful results using the same. EEG signals are usually captivated to diagnose the problems related to the electrical activities of the brain as it tracks and records brain wave patterns to produce a definitive report on seizure activities of the brain. While exercising machine learning practices, various data preprocessing techniques were implemented to attain cleansed and organized data to predict better results and higher accuracy. Section II gives a comprehensive presurvey of existing work performed so far on the same; furthermore, section III sheds light on the dataset used for this research.</b></p>

The Investigation of Behavior Change in EEG Signals During Induction of Anesthesia

2021 ◽  
Author(s):  
Shangkun Li ◽  
Wei Dan ◽  
Lihao Chen ◽  
Bin Wu ◽  
Li Ren ◽  
...  
Keyword(s):  
Time Windows ◽  
Age Groups ◽  
Electrical Signal ◽  
Anesthesia Induction ◽  
Specific Time ◽  
Eeg Signals ◽  
Brain Wave ◽  
Density Spectrum ◽  
Anesthetic Drugs ◽  
Anesthesia Depth

Anesthesiology aims to make anesthesia safer and increase the precision of prognoses. Correct assessment of the anesthesia depth is crucial to its safety. At present, intraoperative electroencephalogram (EEG) monitoring is the primary mode of anesthesia depth monitoring and judgment. However, most clinical anesthesiologists rely on commercial anesthesia depth monitors to judge anesthesia depth, such as bispectral index (BIS) and patient state index (PSI). This may lack an understanding of associated changes in brain wave quantization. Therefore, this study conducts quantitative analyses of EEG signals during anesthesia induction. EEG signals are processed within specific time windows and extracted brainpower density spectrum arrays with different frequency bands, brain electrical signal spectra, source frequencies and other key indicators. Analysis and comparison of these indicators clarifies patterns of variation in EEG signals during early anesthesia induction. The spectral edge frequencies (SEFs) of EEG signals within different time windows can be modeled accurately, from which the specific time points of EEG signal changes are derived. Furthermore, the relationship between patient age and the effect of anesthetic drugs is preliminarily investigated by analyzing the SEF variations of different age groups. This study quantifies changes in the EEG signals of patients at the initial stage of anesthesia induction and drug-related effects are observed, which opens a way for further exploration of EEG changes in patients under general anesthesia.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

Method of estimation of hidden correlation of narrowband noise signals

2019 ◽  
pp. 34-39 ◽  
Author(s):  
E.I. Chernov ◽  
N.E. Sobolev ◽  
A.A. Bondarchuk ◽  
L.E. Aristarhova
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
New Technology ◽  
Digital Signal ◽  
Measuring Instruments ◽  
Central Frequency ◽  
Pearson Criterion ◽  
Useful Signal ◽  
Band Limited ◽  
Narrowband Noise

The concept of hidden correlation of noise signals is introduced. The existence of a hidden correlation between narrowband noise signals isolated simultaneously from broadband band-limited noise is theoretically proved. A method for estimating the latent correlation of narrowband noise signals has been developed and experimentally investigated. As a result of the experiment, where a time frag ent of band-limited noise, the basis of which is shot noise, is used as the studied signal, it is established: when applying the Pearson criterion, there is practically no correlation between the signal at the Central frequency and the sum of signals at mirror frequencies; when applying the proposed method for the analysis of the same signals, a strong hidden correlation is found. The proposed method is useful for researchers, engineers and metrologists engaged in digital signal processing, as well as developers of measuring instruments using a new technology for isolating a useful signal from noise – the method of mirror noise images.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

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