Prognostication of Human Brain EEG Signal Dynamics Using a Refined Coupled Oscillator Energy Exchange Model

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.

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Quality analysis for reliable complex multiclass neuroscience signal classification via electroencephalography

International Journal of Quality & Reliability Management ◽

10.1108/ijqrm-07-2021-0237 ◽

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.

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

Fractals ◽

10.1142/s0218348x1850069x ◽

2018 ◽

Vol 26 (05) ◽

pp. 1850069 ◽

Cited By ~ 30

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.

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