Recently, the application of Independent Component Analysis (ICA) to natural images has raised a great interest. Some outstanding features have been observed, like the sparse distribution of the independent components and the special appearance of the ICA bases (most of them look like edges). This paper provides a new insight on this behaviour, being supported by experimental results. In partic...

Atrial Fibrillation is the most common sustained arrhythmia encountered by clinicians. Because of the invisible waveform of atrial fibrillation in atrial activation for human, it is necessary to develop an automatic diagnosis system. 12-Lead ECG now is available in hospital and is appropriate for using Independent Component Analysis to estimate the AA period. In this research, we also adopt a s...

In this paper, we propose a method for blind source separation (BSS) of convolutive audio recordings with short blocks of stationary sources, i.e. dynamically changing source activity but no source movements.It consists of a time-frequency sparseness based localization step to identify segments with stationary sources whose number is equal to the number of microphones. We then use a frequency d...

Independent component analysis (ICA) attempts to nd a linear decomposition of observed data vectors into components that are statistically independent. It is well known, however, that such a decomposition cannot be exactly found, and in many practical applications, independence is not achieved even approximately. This raises the question on the utility and interpretation of the components given...

One of the reasons ICA (Independent Component Analysis) became so popular is that ICA is a promising tools for a lot of applications. One of the attractive applications is the biological data analysis. There are a lot of works on neurobiological data analysis such as EEG (Electroencephalography), fMRI (functional Magnetic Resonance Imaging), and MEG (Magnetoencephalography), and they show inter...

In this paper we present a new scheme for Palmprint verification. The proposed method can be viewed as a combination of Gaussian Mixture Model (GMM) followed by Independent Component Analysis (ICA I and ICA II) applied directly on the pixels. This approach follows the path opened by previous works making use of GMM followed by Principal Component Analysis (PCA) and Linear Discriminate Analysis ...

SUMMARY Independent component analysis (ICA) has recently been applied to epileptic seizure in the EEG. In this paper, the authors show how the fundamental axioms required for ICA to be valid are broken. Four common cases of childhood seizure are presented and assessed for stationarity and an eigenvalue analysis is applied. In all cases, for the stationary sections of data the eigenvalue analys...

The features of human lip motion from video clips are extracted by three unsupervised learning algorithms, i.e., Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Non-negative Matrix Factorization (NMF). Since the human perception of facial motion goes through two different pathways, i.e., the lateral fusifom gyrus for the invariant aspects and the superior temporal ...

<p><span>Alzheimer merupakan salah satu jenis penyakit demensia yang ditandai dengan penurunan fungsi otak secara perlahan mulai dari ingatan sampai pada fisik. Diagnosis Alzheimer dapat dilakukan melalui analisis sinyal hasil rekaman EEG (Electroencephalogram). Namum, masalah utama dihadapi dalam memahami adalah terukur campuran antara dan <em>artifact.</em> <em>A...

Independent component analysis (ICA) of an image sequence extracts a set of statistically independent images, and deenes a corresponding set of unconstrained dual time courses. However, the extra degrees of freedom implicit in these time courses can lead to physically improbable solutions. Accordingly, we introduce two methods for regularising ICA: smoothed independent component analysis (smICA...