Independent component approach to the analysis of EEG and MEG recordings

2000 ◽  
Vol 47 (5) ◽  
pp. 589-593 ◽  
Author(s):  
R. Vigario ◽  
J. Sarela ◽  
V. Jousmiki ◽  
M. Hamalainen ◽  
E. Oja
Keyword(s):  
Independent Component ◽  
Component Approach

Independent component approach to the analysis of EEG recordings at early stages of depressive disorders

2010 ◽  
Vol 121 (3) ◽  
pp. 281-289 ◽  
Author(s):  
Vera A. Grin-Yatsenko ◽  
Ineke Baas ◽  
Valery A. Ponomarev ◽  
Juri D. Kropotov
Keyword(s):  
Depressive Disorders ◽  
Independent Component ◽  
Early Stages ◽  
Component Approach ◽  
Eeg Recordings

scholarly journals EMBEDDED IMPLEMENTATION OF EEG ANALYSIS USING INDEPENDENT COMPONENT APPROACH

2012 ◽  
pp. 11-16
Author(s):  
FASEELA.K. P ◽  
SUPRIYA. P
Keyword(s):  
Brain Regions ◽  
Independent Component ◽  
Eeg Analysis ◽  
Eeg Signals ◽  
Brain Signals ◽  
Component Approach ◽  
Embedded Development ◽  
Ica Algorithm ◽  
Embedded Implementation ◽  
Original Information

Brain signals are important in diagnosing various disorders and abnormalities in the human body. These signals are recorded by scalp electrodes and are called as EEG signals. EEG signals are a mixture of signals from different brain regions which contain artefacts along with original information. These contaminated mixtures are analysed such that diagnosis of various diseases is possible. One of the effective methods available is Independent Component Analysis (ICA) for removing artefacts and for separation and analysis of the desired sources from within the EEGs. This paper focuses on the analysis of EEG signals using ICA approach. Two ICA algorithms- Pearson ICA and JADE ICA are analysed in this paper. Comparison of these ICA algorithms in removing artefacts from EEG has been carried out by simulation using MATLAB. Then the Pearson ICA algorithm simulation is done using Visual C#. The algorithm has been implemented in an Embedded Development Kit (EDK) using .NET Micro Framework and the results are presented.

scholarly journals Bi-Smoothed Functional Independent Component Analysis for EEG Artifact Removal

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1243
Author(s):  
Marc Vidal ◽  
Mattia Rosso ◽  
Ana M. Aguilera 
Keyword(s):  
Independent Component Analysis ◽  
Principal Component ◽  
R Package ◽  
Component Analysis ◽  
Optimal Number ◽  
Independent Component ◽  
Tuning Parameters ◽  
Neural Origin ◽  
Component Approach ◽  
Functional Representations

Motivated by mapping adverse artifactual events caused by body movements in electroencephalographic (EEG) signals, we present a functional independent component analysis based on the spectral decomposition of the kurtosis operator of a smoothed principal component expansion. A discrete roughness penalty is introduced in the orthonormality constraint of the covariance eigenfunctions in order to obtain the smoothed basis for the proposed independent component model. To select the tuning parameters, a cross-validation method that incorporates shrinkage is used to enhance the performance on functional representations with a large basis dimension. This method provides an estimation strategy to determine the penalty parameter and the optimal number of components. Our independent component approach is applied to real EEG data to estimate genuine brain potentials from a contaminated signal. As a result, it is possible to control high-frequency remnants of neural origin overlapping artifactual sources to optimize their removal from the signal. An R package implementing our methods is available at CRAN.

INDEPENDENT COMPONENT APPROACH TO THE ANALYSIS OF HAND GESTURE sEMG AND FACIAL sEMG

2008 ◽  
Vol 20 (02) ◽  
pp. 83-93 ◽  
Author(s):  
Ganesh R. Naik ◽  
Dinesh K. Kumar ◽  
Sridhar P. Arjunan ◽  
Marimuthu Palaniswami
Keyword(s):  
Electrical Activity ◽  
Statistical Signal Processing ◽  
Independent Component ◽  
Hand Gestures ◽  
Multivariate Statistical ◽  
Surface Electromyogram ◽  
Facial Muscles ◽  
Analysis Technique ◽  
Component Approach ◽  
Semg Signals

Independent component analysis algorithm, a recently developed multivariate statistical data analysis technique, has been successfully used for signal extraction in the field of biomedical and statistical signal processing. This paper reviews the concept of ICA and demonstrates its usefulness and limitations in the context of surface electromyogram related to hand movements and facial muscles. In the first experiment, ICA has been used to separate the electrical activity from different hand gestures. The second part of our study considers separating electrical activity from facial muscles. In both instances, surface electromyogram has been used as an indicator of muscle activity. The theoretical analysis and experimental results demonstrate that ICA is suitable for the identification of different hand gestures using sEMG signals. The results identify the unsuitability of ICA when the similar techniques are used for the facial muscles in order to perform different vowel classification. This technique could be used as a prerequisite tool to measure the reliability of sEMG based systems in rehabilitations and human computer interaction applications.

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