scholarly journals Study on method of filtering noises from electroencephalography signals and its application for identification of several electroencephalography signals

2017 ◽  
Vol 1 (T4) ◽  
pp. 95-104
Author(s):  
Tuan Van Huynh ◽  
Vu Quang Huynh
Keyword(s):  
Independent Component Analysis ◽  
Digital Filters ◽  
Component Analysis ◽  
Independent Component ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
Body Movements ◽  
Different Types ◽  
The Status ◽  
The Mean

Electroencephalographic (EEG) signals have usually been affected by different types of noise as 50 Hz noise, mechanical noise caused by body movements, heart disturbance, eye noise... In this paper, methods such as: independent component analysis (independent component analysis-ICA), discrete wavelet transform and design of digital filters, were used to filter the noises, to classify the basic components for EEG signals. Then the mean of energy value was calculated to identify the status of the EEG signals such as blink, thoughts, emotion, smoking and blood pressure. The results of calculations and simulations of signals EEG could demonstrate the efficiency of the method.

MODEL-FREE FUNCTIONAL MRI ANALYSIS USING TOPOGRAPHIC INDEPENDENT COMPONENT ANALYSIS

2004 ◽  
Vol 14 (04) ◽  
pp. 217-228 ◽  
Author(s):  
ANKE MEYER-BÄSE ◽  
OLIVER LANGE ◽  
AXEL WISMÜLLER ◽  
HELGE RITTER
Keyword(s):  
Independent Component Analysis ◽  
Slight Modification ◽  
Component Analysis ◽  
Independent Component ◽  
Self Organizing Map ◽  
Analysis Techniques ◽  
Model Free ◽  
Temporal Domain ◽  
Different Types ◽  
Fmri Analysis

Data-driven fMRI analysis techniques include independent component analysis (ICA) and different types of clustering in the temporal domain. Since each of these methods has its particular strengths, it is natural to look for an approach that unifies Kohonen's self-organizing map and ICA. This is given by the topographic independent component analysis. While achieved by a slight modification of the ICA model, it can be at the same time used to define a topographic order (clusters) between the components, and thus has the usual computational advantages associated with topographic maps. In this contribution, we can show that when applied to fMRI analysis it outperforms FastICA.

Digital Audio Watermarking Scheme Based on Independent Component Analysis in DWT Domain

2011 ◽  
Vol 219-220 ◽  
pp. 1121-1125 ◽  
Author(s):  
Rui Chen ◽  
Yu Lin Lan ◽  
Reza Asharif Mohammad
Keyword(s):  
Independent Component Analysis ◽  
Low Frequency ◽  
Component Analysis ◽  
Extraction Process ◽  
Independent Component ◽  
Audio Watermarking ◽  
Discrete Wavelet ◽  
Digital Audio ◽  
Digital Audio Watermarking ◽  
Watermarking Scheme

This paper proposed a digital audio watermarking scheme based on independent component analysis (ICA) in DWT domain. The embedding process make full use of the multi-resolution characteristic of discrete wavelet transform (DWT), performing 3-level DWT. Selecting the low frequency coefficient appropriately as the embed location to make sure of the balance between the transparency and robustness. Then constructing the ICA model to embed the watermarking. The extraction process is similar with ICA’s goal, it’s used in extraction makes the scheme simple for implementation. The experiment results show that the proposed scheme has good robustness against common attacks, as well as transparency.

scholarly journals High impedance fault detection in 11 kV overhead line with discrete wavelet transform and independent component analysis

2021 ◽  
Vol 24 (2) ◽  
pp. 661
Author(s):  
Md Ferdouse Hossain Bhuiya ◽  
Rohaiza Hamdan ◽  
Dur Mohammad Soomro ◽  
Abdelrehman Omer Idris ◽  
Hussain Sharif
Keyword(s):  
Wavelet Transform ◽  
Fault Detection ◽  
Independent Component Analysis ◽  
Discrete Wavelet Transform ◽  
Component Analysis ◽  
Independent Component ◽  
Discrete Wavelet ◽  
Advanced Technique ◽  
High Impedance ◽  
High Impedance Fault

This paper proposes an analysis of high-impedance fault detection algorithms for medium voltage distribution lines based on the discrete wavelet transform (DWT) technique and a more advanced technique named independent component analysis (ICA) independently. Three-phase distribution line model and two diodes high impedance fault model, which represents the unsymmetrical fault current of electric arc, simulated using MATLAB/Simulink. High impedance fault (HIF) detection algorithm initially analyzes the sampled current waveforms through DWT and the resultant third level high-frequency components “d3” coefficients are analyzed through one cycle moving window approach. The proposed algorithm successfully detects any HIF in the distribution current even if there is a slight or no difference in the amplitude of the HIF and the waveform of the phase current. On the other hand, the ICA more developed algorithm than DWT successfully separated the noise signals from the obtained current waveforms and HIF noise signals can be differentiated with non-HIF noise signals. Because of this reason ICA is chosen in this research. The detected HIF current can be from 50 ma and up.

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