Research on the Relation of EEG Signal Chaos Characteristics with High-Level Intelligence Activity of Human Brain

2011 ◽  
pp. 50-65
Author(s):  
X Wang ◽  
M Juan ◽  
G Tan ◽  
L Zou
Keyword(s):  
Human Brain ◽  
Eeg Signal ◽  
Intelligence Activity ◽  
High Level

A Comprehensive Analysis on EEG Signal Classification on Human Brain

2021 ◽  
Author(s):  
Puranam Revanth Kumar ◽  
Sangeetha K ◽  
Rabi Narayan Satpathy ◽  
Sachi Nandan Mohanty
Keyword(s):  
Human Brain ◽  
Comprehensive Analysis ◽  
Signal Classification ◽  
Eeg Signal ◽  
Eeg Signal Classification

scholarly journals Low Frequency Noise Remove from EEG Signal

2020 ◽  
Vol 9 (1) ◽  
pp. 1510-1513
Keyword(s):  
Human Brain ◽  
Random Noise ◽  
Low Frequency ◽  
Frequency Noise ◽  
Eeg Signal ◽  
Low Frequency Noise ◽  
Frequency Range ◽  
Average Value ◽  
Different Types ◽  
The Brain

The electrical activity of the brain recorded by EEG which used to detect different types of diseases and disorders of the human brain. There is contained a large amount of random noise present during EEG recording, such as artifacts and baseline changes. These noises affect the low -frequency range of the EEG signal. These artifacts hiding some valuable information during analyzing of the EEG signal. In this paper we used the FIR filter for removing low -frequency noise(<1Hz) from the EEG signal. The performance is measured by calculating the SNR and the RMSE. We obtained RMSE average value from the test is 0.08 and the SNR value at frequency(<1Hz) is 0.0190.

scholarly journals Identification of optimum segment in single channel EEG biometric system

2021 ◽  
Vol 23 (3) ◽  
pp. 1847
Author(s):  
Muhammad Afif Hendrawan ◽  
Pramana Yoga Saputra ◽  
Cahya Rahmad
Keyword(s):  
Single Channel ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Alpha Band ◽  
Biometric System ◽  
Linear Discriminant ◽  
Power Spectral ◽  
Eyes Open ◽  
High Level

Nowadays, biometric modalities have gained popularity in security systems. Nevertheless, the conventional commercial-grade biometric system addresses some issues. The biggest problem is that they can be imposed by artificial biometrics. The electroencephalogram (EEG) is a possible solution. It is nearly impossible to replicate because it is dependent on human mental activity. Several studies have already demonstrated a high level of accuracy. However, it requires a large number of sensors and time to collect the signal. This study proposed a biometric system using single-channel EEG recorded during resting eyes open (EO) conditions. A total of 45 EEG signals from 9 subjects were collected. The EEG signal was segmented into 5 second lengths. The alpha band was used in this study. Discrete wavelet transform (DWT) with Daubechies type 4 (db4) was employed to extract the alpha band. Power spectral density (PSD) was extracted from each segment as the main feature. Linear discriminant analysis (LDA) and support vector machine (SVM) were used to classify the EEG signal. The proposed method achieved 86% accuracy using LDA only from the third segment. Therefore, this study showed that it is possible to utilize single-channel EEG during a resting EO state in a biometric system.

Quality analysis for reliable complex multiclass neuroscience signal classification via electroencephalography

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ashutosh Shankhdhar ◽  
Pawan Kumar Verma ◽  
Prateek Agrawal ◽  
Vishu Madaan ◽  
Charu Gupta
Keyword(s):  
Human Brain ◽  
Network Architecture ◽  
Neural Control ◽  
Human Life ◽  
Binary Classification ◽  
Sampling Rate ◽  
Quality Analysis ◽  
Eeg Signal ◽  
Content Type

PurposeThe aim of this paper is to explore the brain–computer interface (BCI) as a methodology for generating awareness and increasing reliable use cases of the same so that an individual's quality of life can be enhanced via neuroscience and neural networks, and risk evaluation of certain experiments of BCI can be conducted in a proactive manner.Design/methodology/approachThis paper puts forward an efficient approach for an existing BCI device, which can enhance the performance of an electroencephalography (EEG) signal classifier in a composite multiclass problem and investigates the effects of sampling rate on feature extraction and multiple channels on the accuracy of a complex multiclass EEG signal. A one-dimensional convolutional neural network architecture is used to further classify and improve the quality of the EEG signals, and other algorithms are applied to test their variability. The paper further also dwells upon the combination of internet of things multimedia technology to be integrated with a customized design BCI network based on a conventionally used system known as the message query telemetry transport.FindingsAt the end of our implementation stage, 98% accuracy was achieved in a binary classification problem of classifying digit and non-digit stimuli, and 36% accuracy was observed in the classification of signals resulting from stimuli of digits 0 to 9.Originality/valueBCI, also known as the neural-control interface, is a device that helps a user reliably interact with a computer using only his/her brain activity, which is measured usually via EEG. An EEG machine is a quality device used for observing the neural activity and electric signals generated in certain parts of the human brain, which in turn can help us in studying the different core components of the human brain and how it functions to improve the quality of human life in general.

FRACTAL-BASED CLASSIFICATION OF HUMAN BRAIN RESPONSE TO LIVING AND NON-LIVING VISUAL STIMULI

Fractals ◽  
2018 ◽  
Vol 26 (05) ◽  
pp. 1850069 ◽  
Author(s):  
MOHAMMAD ALI AHMADI-PAJOUH ◽  
TIRDAD SEIFI ALA ◽  
FATEMEH ZAMANIAN ◽  
HAMIDREZA NAMAZI ◽  
SAJAD JAFARI
Keyword(s):  
Human Brain ◽  
Human Behavior ◽  
Fractal Structure ◽  
Brain Activity ◽  
Visual Stimuli ◽  
Chaotic Signal ◽  
Eeg Signal ◽  
Brain Response ◽  
Human Eeg ◽  
Structure Complexity

Analysis of human behavior is one of the major research topics in neuroscience. It is known that human behavior is related to his brain activity. In this way, the analysis of human brain activity is the root for analysis of his behavior. Electroencephalography (EEG) as one of the most famous methods for measuring brain activity generates a chaotic signal, which has fractal characteristic. This study reveals the relation between the fractal structure (complexity) of human EEG signal and the applied visual stimuli. For this purpose, we chose two types of visual stimuli, namely, living and non-living visual stimuli. We demonstrate that the fractal structure of human EEG signal changes significantly between living versus non-living visual stimuli. The capability observed in this research can be applied to other kinds of stimuli in order to classify the brain response based on the types of stimuli.

scholarly journals Analysis of Golfer‘S Brainwave Signal During Par Tee Ireland and Driving Range Game

2018 ◽  
Vol 7 (4.30) ◽  
pp. 469
Author(s):  
Yusof, M.M. M ◽  
Salleh, S. M ◽  
Ainul, H.M. Y ◽  
Siswanto, W. A ◽  
Mahmud, W.M.A .W
Keyword(s):  
Fourier Transform ◽  
Human Brain ◽  
Brain Computer Interface ◽  
Exchange Process ◽  
Sport Performance ◽  
Computer Interface ◽  
Eeg Signal ◽  
External Device ◽  
Control Signals ◽  
Driving Range

Ways to improve sport performance become exceptional contemporary interest. Nowadays, many studies use human brain as an input signal include eyes blinking, attention and meditation to control the exchange process. Brain–Computer Interface (BCI) requires generating control signals for external device by analysing the internal brain signal. The objective is to identify the signal of brainwave which gives effect to performance of golfer. The analysis involved the meditation (α) and attention (β) state of different golf players. In this project, the brainwave of golfer’s will be analyzed based on the movement before club strike the ball. EEG signal used to find out the features by using Fast Fourier Transform (FFT). The analysis included three categories of player include beginner, intermediate and professional. Two types of game have been considered which are Par Tee Ireland and Driving Range. The project interfaces MATLAB software with an EEG headset. The data has interpreted in time and frequency domain graph that show different level in an attention (β) state for both games. Brainwave signals indicated players’ performance and lead to better performance. This data benefits increasing the performance of golfer to become the professional golfer by using electroencephalography (EEG) headset in future study.

scholarly journals BOLD and EEG signal variability at rest differently relate to aging in the human brain

NeuroImage ◽  
2020 ◽  
Vol 207 ◽  
pp. 116373 ◽  
Author(s):  
D. Kumral ◽  
F. Şansal ◽  
E. Cesnaite ◽  
K. Mahjoory ◽  
E. Al ◽  
...  
Keyword(s):  
Human Brain ◽  
Eeg Signal ◽  
Signal Variability
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