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

Computational Neurobiology Laboratory, Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA 92107, USA.
NeuroImage (Impact Factor: 6.36). 03/2007; 34(4):1443-9. DOI: 10.1016/j.neuroimage.2006.11.004
Source: PubMed

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 ICA-decomposed 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 non-artifact processes themselves.

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Available from: Arnaud Delorme, Aug 30, 2015
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    • "During autocalibration , optimization aimed at collecting N=7 artifact free trials for each mental task. Artifact and outlier rejection was based on statistical metrics of EEG features (thresholding of amplitude, kurtosis and probablity of 4-40 Hz band pass filtered EEG, [14]) and spectral components [7]. BP C ch,f b were extracted from clean imagery EEG segments and grouped according to mental task pair combinations C. "
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    • "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 et al., 2007). "
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    • "Many traditional approaches have been proposed to remove or attenuate artifacts from recorded EEG signals [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19]. The most widely used methods for attenuating artifacts in EEG signals are based on blind source separation such as independent component analysis (ICA) and canonical correlation analysis (CCA) [20], [11], [21], [22], [23], [14], [15], [24]. The BSS-based algorithms assume that the observations are linear mixing of the sources and the number of sources is equal or less than the number of observations. "
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