Exploratory data–driven techniques or Blind Source Separation (BSS) methods in fMRI data analysis are neither based on explicit signal models nor on the a priori knowledge of the underlying physiological process. One such method is Independent Component Analysis (ICA) which searches for stochastically independent signals within the multivariate observations. Recently, a new paradigm in ICA emer...

The pandemic of 2020 brought a lot changes to the health and medical industry where smart devices started flowing in compensate lack hospitals for less severe cases. This work primarily focuses on lung abnormalities as lungs were one first organs break down when virus affected body. whilst not becoming but will act catalyst spread virus’s effect through thus concluding complete pulmonary breakd...

This discussion presents a new perspective of subspace independent component analysis (ICA). The notion of a function of cumulants (kurtosis) is generalized to vector kurtosis. This vector kurtosis is utilized in the subspace ICA algorithm to estimate subspace independent components. One of the main advantages of the presented approach is its computational simplicity. The experiments have shown...

Speech is the fundamental means of communication among humans. Speech production is the process of converting a linguistic message to the acoustic waveform. Separating various linearly mixed speech signals is often modelled by famous cocktail party problem and can be achieved by a technique known as Independent Component Analysis (ICA). ICA is similar to PCA and Factor analysis but it works on ...

Principal component analysis (PCA) is used to reduce dimensionality of electrocardiogram (ECG) data prior to performing independent component analysis (ICA). A newly developed PCA variance estimator by the author has been applied for detecting true, actual and false peaks of ECG data files. In this paper, it is felt that the ability of ICA is also checked for parameterization of ECG signals, wh...

We present new results about the simultaneous linear inverse problems using independent component analysis (ICA), which can be used to separate the data into statistically independent components. The idea of using ICA in solving such inverse problems, especially in EEG/MEG context, has been a known topic for at least more than a decade, but the known results have been justified heuristically, a...

The paper is an overview of the most frequently used neural network algorithms for implementing Independent Component Analysis (ICA). The performance of six structurally different algorithms was ranked in blind separation of independent artificially generated signals using the stationary linear ICA model. Ranking of the estimated components was also carried out and compared among different ICA ...

Independent Component Analysis (ICA) is a multivariate data analysis process largely sudied these last years in the signal processing community for blind source separation. This paper proposes to show the interest of ICA as a tool for unsupervised analysis of hyperspectral images. The commonly used Principal Component Analysis (PCA) is the mean square optimal projection for gaussian data leadin...

It has previously been suggested that the visual cortex performs a data analysis similar to independent component analysis (ICA). Following this idea we show that an incomplete ICA, applied after filtering, can be used to detect objects in natural scenes. Based on this we show that an incomplete ICA can be used to efficiently cluster independent components. We further apply this algorithm to to...

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 t...