Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis

Abstract

Detecting artifacts produced in EEG data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG research. It is now widely accepted that independent component analysis (ICA) may be a useful tool for isolating artifacts and/or cortical processes from electroencephalographic (EEG) data. We present results of simulations demonstrating that ICA decomposition, here tested using three popular ICA algorithms Infomax, SOBI, and FastICA, can allow more sensitive automated detection of small non-brain artifacts than applying the same detection methods directly to the scalp channel data. We tested the upper bound performance of five methods for detecting various types of artifacts by separately optimizing and then applying them to artifact-free EEG data into which we had added simulated artifacts of several types, ranging in size from thirty times smaller (−50 dB) to the size of the EEG data themselves (0 dB). Of the methods tested, those involving spectral thresholding were most sensitive. Except for muscle artifact detection where we found no gain of using ICA, all methods proved more sensitive when applied to the ICAdecomposed data than applied to the raw scalp data: the mean performance for ICA was higher and situated at about two standard deviations away from the performance distribution obtained on raw data. We note that ICA decomposition also allows simple subtraction of artifacts accounted for by single independent components, and/or separate and direct examination of the decomposed nonartifact processes themselves.

Integration of EEG source imaging and fMRI during continuous viewing of natural movies

“…Taken together, these EEG source imaging results indicate that the artifact-correction methodology correctly identified and removed unwanted nonneural components of the EEG signal while preserving those containing information which reflected the movie stimulus. While these three relatively straightforward criteria seemed to reliably remove the strongest artifactual components, further study is needed for delineating more subtle artifacts from true neural activity [38].…”

Resting state glutamate predicts elevated pre-stimulus alpha during self-relatedness: A combined EEG-MRS study on “rest-self overlap”

“…Epochs with irregular noise were identified and rejected using a computer algorithm based on abnormal statistical distribution, as well as by inferences from visual inspection (Delorme, Sejnowski, & Makeig, 2007). Typical physiological artifacts (e.g., eye blinks, eye movement, and muscle potentials) were retained for the independent component analysis (ICA).…”

Saliency or template? ERP evidence for long-term representation of word stress

“…Eyemovement artifacts were corrected with the help of independent component analysis (ICA). ICA is a method capable of blindly separating signals having different sources, and therefore it is suggested to be efficient for separating the EEG signal from noncephalic artifacts (Delorme et al, 2007). In order to correct eye-movement artifacts, the raw EEG was first decomposed into ICA components using the Infomax algorithm, and then 2-5 components related to eye-movements were selected by visual inspection by an expert, relying on both the time course and the spatial maps of the components.…”