Paper Info
Title
Automatic Identification of Artifact-related Independent Components for Artifact Removal in EEG Recordings.
Abstract
Electroencephalography (EEG) is the recording of electrical activity produced by the firing of neurons within the brain. These activities can be decoded by signal processing techniques. However, EEG recordings are always contaminated with artifacts which hinder the decoding process. Therefore, identifying and removing artifacts is an important step. Researchers often clean EEG recordings with assistance from Independent Component Analysis (ICA), since it can decompose EEG recordings into a number of artifact-related and event related potential (ERP)-related independent components (ICs). However, existing ICA-based artifact identification strategies mostly restrict themselves to a subset of artifacts, e.g. identifying eye movement artifacts only, and have not been shown to reliably identify artifacts caused by non-biological origins like high-impedance electrodes. In this paper, we propose an automatic algorithm for the identification of general artifacts. The proposed algorithm consists of two parts: 1) an event-related feature based clustering algorithm used to identify artifacts which have physiological origins and 2) the electrode-scalp impedance information employed for identifying non-biological artifacts. The results on EEG data collected from 10 subjects show that our algorithm can effectively detect, separate, and remove both physiological and non-biological artifacts. Qualitative evaluation of the reconstructed EEG signals demonstrates that our proposed method can effectively enhance the signal quality, especially the quality of ERPs, even for those that barely display ERPs in the raw EEG. The performance results also show that our proposed method can effectively identify artifacts and subsequently enhance the classification accuracies compared to four commonly used automatic artifact removal methods.
Year
DOI
Venue
2016
10.1109/JBHI.2014.2370646
IEEE journal of biomedical and health informatics
Keywords
Field
DocType
electroencephalography (eeg),independent component analysis (ica),event-related potential (erp),electrode-scalp impedance,hierarchical clustering,impedance measurement,physiology,feature extraction,impedance,electroencephalography,electrodes
Hierarchical clustering,Signal processing,Computer vision,Pattern recognition,Computer science,Feature extraction,Eye movement,Artificial intelligence,Independent component analysis,Decoding methods,Cluster analysis,Electroencephalography
Journal
Volume
Issue
ISSN
PP
99
2168-2208
Citations 
PageRank 
References 
10
0.65
6
Authors
3
Name
Order
Citations
PageRank
Yuan Zou1100.65
Viswam Nathan25014.09
Roozbeh Jafari398793.51