Automatic Identification of Artifact-Related Independent Components for Artifact Removal in EEG Recordings
نویسندگان
چکیده
منابع مشابه
Semi-automatic identification of independent components representing EEG artifact.
OBJECTIVE Independent component analysis (ICA) can disentangle multi-channel electroencephalogram (EEG) signals into a number of artifacts and brain-related signals. However, the identification and interpretation of independent components is time-consuming and involves subjective decision making. We developed and evaluated a semi-automatic tool designed for clustering independent components fro...
متن کاملArtifact 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 ...
متن کاملIterative 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 ...
متن کاملArtifact Removal from EEG Signals
Electroencephalographic (EEG) recordings are often contaminated with several artifacts.Powerline interference and baseline noise is always present in EEG response of every patient. A number of strategies are available to deal with noise effectively both at the time of EEG recording as well as during preprocessing of recorded data. The aim of the paper is to give an overview of the most common s...
متن کاملClassification of independent components of EEG into multiple artifact classes.
In this study, we aim to automatically identify multiple artifact types in EEG. We used multinomial regression to classify independent components of EEG data, selecting from 65 spatial, spectral, and temporal features of independent components using forward selection. The classifier identified neural and five nonneural types of components. Between subjects within studies, high classification pe...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Journal of Biomedical and Health Informatics
سال: 2016
ISSN: 2168-2194,2168-2208
DOI: 10.1109/jbhi.2014.2370646