The electrically-evoked compound action potential (ECAP) is the synchronous whole auditory nerve activity in response to an electrical stimulus, and can be recorded in situ on cochlear implant (CI) electrodes. A novel procedure (ECAP-ICA) to isolate the ECAP from the stimulation artifact, based on independent component analysis (ICA), is described here. ECAPs with artifact (raw-ECAPs) were sequ...

Independent Component Analysis (ICA) is intended to recover the mutually independent sources from their linear mixtures, and F astICA one of most successful ICA algorithms. Although it seems reasonable improve performance by introducing more nonlinear functions negentropy estimation, original fixed-point method (approximate Newton method) in degenerates under this circumstance. To alleviate pro...

Independent Component Analysis (ICA) and Gabor wavelets extract the most discriminating features for facial action unit classification by employing either a cosine similarity measure (CSM) classifier or support vector machines (SVMs). So far, only the ICA approach, which is based on the InfoMax principle, has been tested for facial expression recognition. In this paper, in addition to the InfoM...

Dimensional reduction methods have significantly improved the simplification of Pulsed Thermography (PT) data while improving accuracy results. Such approaches reduce quantity to analyze and improve contrast main defects in samples contributed their popularity. Many works been proposed literature mainly based on Principal Component (PCT). Recently Independent Analysis (ICA) has a topic attentio...

Independent component analysis (ICA) the theory of mixed, independent, non-Gaussian sources has a central role in signal processing, computer vision and pattern recognition. One of the most fundamental conjectures of this research eld is that independent subspace analysis (ISA) the extension of the ICA problem, where groups of sources are independent can be solved by traditional ICA followed by...

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...

Independent Component Analysis (ICA) technique separates mixed signals blindly without any information of mixing system. The present work studies and analyses the issues involved in interference rejection in direct sequence spread spectrum communication systems based on Independent Component Analysis technique. The ICA technique tries to separate the unwanted interfering signal from the desired...

In this paper we propose a new hybrid unsupervised / supervised learning scheme that integrates Independent Component Analysis (ICA) with the SupportVector Machine (SVM) approach and apply this new learning scheme to the face detection problem. In low-level feature extraction, ICA produces independent image bases that emphasize edge information in the image data. In high-level classification, S...

In this paper, independent component analysis (ICA) is used for blind source separation of biomedical signals. Visual and quantitative tests of the ability of ICA to separate signals were performed using a fast ICA algorithm. Results obtained from simulated and FECG signals show that the ICA performance using the whitening matrix of the mixed signals was superior to that of random initial weights.

Post-nonlinear (PNL) independent component analysis (ICA) is a generalisation of ICA where the observations are assumed to have been generated from independent sources by linear mixing followed by component-wise scalar nonlinearities. Most previous PNL ICA algorithms require the post-nonlinearities to be invertible functions. In this paper, we present a variational Bayesian approach to PNL ICA ...