scholarly journals EEG Calmness Index Establishment Using Computational of Z-Score

2018 ◽  
Vol 7 (4.11) ◽  
pp. 44
Author(s):  
S. A. M. Aris1 ◽  
N. A. Bani ◽  
M. N.Muhtazaruddin ◽  
M. N. Taib
Keyword(s):  
Signal Processing ◽  
Asymmetry Index ◽  
Eeg Signal ◽  
Z Score ◽  
Fuzzy C Means ◽  
Processing Techniques ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Signal Processing Techniques ◽  
Brain Condition

A lot of useful information can be obtained through observation of the electroencephalogram (EEG) signal such as the human psychophysiology. It has been proven that EEG is handy in human diagnosis and tools to observe the brain condition. The study aims to establish a calmness index, which can differentiate the calmness level of an individual. Alpha waves were selected as the data features and computed into asymmetry index. The data features were clustered using Fuzzy C-Means (FCM) and resulted in three clusters. Wilcoxon Signed Ranks test was applied to determine the significance of the data features clustered by FCM. The Z-score obtained successfully distinguish three level of calmness index from the lower index until the higher index. With the advancement of signal processing techniques, the feature extractions for calmness index establishment computation is achievable.  

scholarly journals Demystifying signal processing techniques to extract resting-state EEG features for psychologists

2020 ◽  
Vol 6 (3) ◽  
pp. 189-209 ◽  
Author(s):  
Zhenjiang Li ◽  
Libo Zhang ◽  
Fengrui Zhang ◽  
Ruolei Gu ◽  
Weiwei Peng ◽  
...  
Keyword(s):  
Signal Processing ◽  
Resting State ◽  
Neural Dynamics ◽  
Complex Signal ◽  
Eeg Signal ◽  
Microstate Analysis ◽  
Processing Techniques ◽  
The Brain ◽  
Signal Processing Techniques ◽  
Eeg Signal Processing

Electroencephalography (EEG) is a powerful tool for investigating the brain bases of human psychological processes non‐invasively. Some important mental functions could be encoded by resting‐state EEG activity; that is, the intrinsic neural activity not elicited by a specific task or stimulus. The extraction of informative features from resting‐state EEG requires complex signal processing techniques. This review aims to demystify the widely used resting‐state EEG signal processing techniques. To this end, we first offer a preprocessing pipeline and discuss how to apply it to resting‐state EEG preprocessing. We then examine in detail spectral, connectivity, and microstate analysis, covering the oft‐used EEG measures, practical issues involved, and data visualization. Finally, we briefly touch upon advanced techniques like nonlinear neural dynamics, complex networks, and machine learning.

scholarly journals Methods for the Analysis of EEG signals: A Review

2021 ◽  
Vol 9 (9) ◽  
pp. 873-876
Author(s):  
Malika Garg
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Electrical Activity ◽  
Signal Analysis ◽  
Eeg Signal ◽  
Eeg Analysis ◽  
Eeg Signals ◽  
Processing Techniques ◽  
The Brain ◽  
Eeg Signal Analysis

Abstract: Electroencephalography (EEG) helps to predict the state of the brain. It tells about the electrical activity going on in the brain. Difference of the surface potential evolved from various activities get recorded as EEG. The analysis of these EEG signals is of utmost importance to solve the problems related to the brain. Signal pre-processing, feature extraction and classification are the main steps of the EEG signal analysis. In this article we discussed various processing techniques of EEG signals. Keywords: EEG, analysis, signal processing, feature extraction, classification

scholarly journals Analysis of EEG Signal Processing Techniques based on Spectrograms

2017 ◽  
Vol 145 (1) ◽  
pp. 151-162 ◽  
Author(s):  
Ricardo Ramos ◽  
José Arturo Olvera ◽  
Ivan Olmos
Keyword(s):  
Signal Processing ◽  
Eeg Signal ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Eeg Signal Processing

scholarly journals Characterization of Brain Stroke Using Image and Signal Processing Techniques

2021 ◽  
Author(s):  
Abdullah Alamoudi ◽  
Yousif Abdallah
Keyword(s):  
Signal Processing ◽  
Brain Injuries ◽  
Cross Sectional ◽  
Manual Contour ◽  
Cross Sectional Imaging ◽  
Brain Stroke ◽  
Processing Techniques ◽  
The Brain ◽  
Signal Processing Techniques

Cross-sectional imaging approaches play a key role in assessing bleeding brain injuries. Doctors commonly determine bleeding size and severity in CT and MRI. Separating and identifying artifacts is extremely important in processing medical images. Image and signal processing are used to classify tissues within images closely linked to edges. In CT images, a subjective process takes a stroke ‘s manual contour with less precision. This chapter presents the application of both image and signal processing techniques in the characterization of Brain Stroke field. This chapter also summarizes how to characterize the brain stroke using different image processing algorithms such as ROI based segmentation and watershed methods.

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