Automated Removal of EKG Artifact From EEG Data Using Independent Component Analysis and Continuous Wavelet Transformation

2014 ◽  
Vol 61 (6) ◽  
pp. 1634-1641 ◽  
Author(s):  
Mehdi Bagheri Hamaneh ◽  
Numthip Chitravas ◽  
Kitti Kaiboriboon ◽  
Samden D. Lhatoo ◽  
Kenneth A. Loparo
Keyword(s):  
Independent Component Analysis ◽  
Wavelet Transformation ◽  
Component Analysis ◽  
Independent Component ◽  
Continuous Wavelet ◽  
Continuous Wavelet Transformation ◽  
Eeg Data

Feature Extraction of the Acoustic Emission Signals of Low Carbon Steel Pitting Corrosion Based on Independent Component Analysis and Wavelet Transform

2013 ◽  
Vol 373-375 ◽  
pp. 677-680
Author(s):  
Wei Li ◽  
Yu Li Gong ◽  
Yang Yu
Keyword(s):  
Acoustic Emission ◽  
Carbon Steel ◽  
Independent Component Analysis ◽  
Pitting Corrosion ◽  
Wavelet Transformation ◽  
Low Carbon Steel ◽  
Component Analysis ◽  
Independent Component ◽  
Low Carbon ◽  
Ae Signals

Based on the characteristics of the acoustic emission (AE) signals from low carbon steel pitting corrosion, a new extraction method was proposed with wavelet transformation and independent component analysis. The experiment result shows that the new method can overcome the influence induced by the uncertainty of the independent source of low carbon steel pitting corrosion and good extraction result can be achieved.

scholarly journals Independent Component Analysis and Source Localization on Mobile EEG Data Can Identify Increased Levels of Acute Stress

2017 ◽  
Vol 11 ◽  
Author(s):  
Bryan R. Schlink ◽  
Steven M. Peterson ◽  
W. D. Hairston ◽  
Peter König ◽  
Scott E. Kerick ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Source Localization ◽  
Acute Stress ◽  
Component Analysis ◽  
Independent Component ◽  
Eeg Data

scholarly journals Comparison of Complex-Valued Independent Component Analysis Algorithms for EEG Data

2019 ◽  
Vol 15 (1) ◽  
pp. 1-12
Author(s):  
Ali Al-Saegh
Keyword(s):  
Independent Component Analysis ◽  
Resting State ◽  
Speaker Identification ◽  
Component Analysis ◽  
Independent Component ◽  
Eeg Signals ◽  
Artifact Detection ◽  
Eeg Data ◽  
Ica Algorithm ◽  
Complex Valued

Independent Component Analysis (ICA) has been successfully applied to a variety of problems, from speaker identification and image processing to functional magnetic resonance imaging (fMRI) of the brain. In particular, it has been applied to analyze EEG data in order to estimate the sources form the measurements. However, it soon became clear that for EEG signals the solutions found by ICA often depends on the particular ICA algorithm, and that the solutions may not always have a physiologically plausible interpretation. Therefore, nowadays many researchers are using ICA largely for artifact detection and removal from EEG, but not for the actual analysis of signals from cortical sources. However, a recent modification of an ICA algorithm has been applied successfully to EEG signals from the resting state. The key idea was to perform a particular preprocessing and then apply a complexvalued ICA algorithm. In this paper, we consider multiple complex-valued ICA algorithms and compare their performance on real-world resting state EEG data. Such a comparison is problematic because the way of mixing the original sources (the “ground truth”) is not known. We address this by developing proper measures to compare the results from multiple algorithms. The comparisons consider the ability of an algorithm to find interesting independent sources, i.e. those related to brain activity and not to artifact activity. The performance of locating a dipole for each separated independent component is considered in the comparison as well. Our results suggest that when using complex-valued ICA algorithms on preprocessed signals the resting state EEG activity can be analyzed in terms of physiological properties. This reestablishes the suitability of ICA for EEG analysis beyond the detection and removal of artifacts with real-valued ICA applied to the signals in the time-domain.

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