Bayesian methods for intracellular recordings: electrode artifact compensation and noise removal

Artifact Removal from Eeg Recordings – an Overview

Electrical impulses generated by nerve firings in the brain diffuse through the head and can be measured by electrodes placed on the scalp, is known as electroencephalogram (EEG) and was first measured in humans by Hans Berger in 1929. The EEG gives a coarse view of neural activity and has been used to noninvasively study cognitive processes and the physiology of the brain. The analysis of EEG ...

Iterative Subspace Decomposition for Ocular Artifact Removal from EEG Recordings

In this study, we present a method to remove ocular artifacts from electroencephalographic (EEG) recordings. This method is based on the detection of the EOG activation periods from a reference EOG channel, definition of covariance matrices containing the nonstationary information of the EOG, and applying generalized eigenvalue decomposition (GEVD) onto these matrices to rank the components in ...

Generalized methods and solvers for noise removal

Restoring STM images via Sparse Coding: noise and artifact removal

In this article, we present a denoising algorithm to improve the interpretation and quality of scanning tunneling microscopy (STM) images. Given the high level of self-similarity of STM images, we propose a denoising algorithm by reformulating the true estimation problem as a sparse regression, often termed sparse coding. We introduce modifications to the algorithm to cope with the existence of...

Removal of EEG noise and artifact using blind source separation.

A study was performed to investigate and compare the relative performance of blind signal separation (BSS) algorithms at separating common types of contamination from EEG. The study develops a novel framework for investigating and comparing the relative performance of BSS algorithms that incorporates a realistic EEG simulation with a known mixture of known signals and an objective performance m...