Technical NoteEnhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis
Introduction
In event-related experiments, each data epoch normally represents one or more experimental trials time locked to one or more experimental events of interest. Typically, software for ERP analysis first subtracts a baseline – e.g., the average pre-stimulus potential– from each trial then finds and eliminates ‘bad’ electrodes at which the resulting potential values exceed some pre-defined bound or some level of noise. The remaining ‘good’ electrodes usually include central scalp sites containing mainly brain activity, temporo-parietal sites that may contain temporal muscle artifacts and frontal sites that may contain prominent blinks, eye movement and other muscle artifacts. It is critical to detect such artifacts contaminating event-related EEG data for several reasons. First, artifactual signals often have high amplitudes relative to brain signals. Thus, even if their appearance in the recorded EEG is infrequent, they can bias average evoked potential or other measures computed from the data and, as a consequence, bias or dilute the results of an experiment. In clinical research, however, artifacts may be abundant, limiting the usability of the data altogether.
In most current EEG analysis, single data trials that contain out-of-bounds potential values at single electrodes are selected for rejection from analysis. A problem with the simple thresholding criterion is that it only takes into account low-order signal statistics (minimum and maximum). This rejection method may fail to detect e.g., muscle activity, which typically involves rapid electromyographic (EMG) signals of small to moderate size nor will it detect artifacts generated by small eye blinks. Statistical measures of EEG signals may contain more relevant information about these and other types of artifacts. For instance, linear trend detection may help in isolating current drift. Computing the probability of each data epoch, given the probability distribution of potential values over all epochs, may help in detecting trials with improbable artifacts. A 4th order moment of the data distribution, its kurtosis, may detect activity distribution indicative of some artifacts. Finally, standard threshold detection methods applied to the single trial data spectra may help in detecting artifacts with specific spectral signatures.
Independent component analysis (ICA) (Bell and Sejnowski, 1995, Jung et al., 2001, Makeig et al., 1996) applied to concatenated collections of single-trial EEG data has also proven to be an efficient method for separating distinct artifactual processes including eye blink, muscle and electrical artifacts (Barbati et al., 2004, Delorme et al., 2001, Iriarte et al., 2003, James and Gibson, 2003, Joyce et al., 2004, Jung et al., 2000b, Tran et al., 2004, Urrestarazu et al., 2004, Zhukov et al., 2000). Although several ICA algorithms in different implementations have been used to separate artifacts from EEG and MEG data, they all can be derived from related mathematical principles (Lee et al., 2000). While ICA is now considered an important technique for detecting artifacts, there are still few quantitative measures of the advantage for artifact detection that is gained from ICA decomposition.
Here we develop a framework for comparing artifact detection methods and use it to determine whether preprocessing EEG data using ICA can help in detecting brief data epochs that contain artifacts. We first apply a set of statistical and spectral analysis methods to detect artifacts in the raw data, optimizing a free parameter for each method so as to optimally detect known artifactual data epochs. Then, we apply the same procedure to the data decomposed using ICA. Finally, we quantitatively compare results of these artifact detection methods applied either to raw or to ICA-preprocessed data.
Section snippets
Methods of artifact detection
We compared five different methods for detecting trials containing artifacts (Barbati et al., 2004, Delorme et al., 2001):
1. Extreme values. First, we used standard thresholding of potential values. Here, data trials were labeled as artifactual if the absolute value of any data point in the trial exceeded a fixed threshold. This method is currently the most widely used artifact detection method in the EEG community. It is most effective for detecting gross eye blinks or eye movement artifacts.
Data simulation
To test and optimize the artifact detection process, we used event-related EEG data from a ‘Go/Nogo’ visual categorization task (Delorme et al., 2004). EEG was recorded at a 1000 Hz sampling rate using a 32-electrode scalp montage with all channels referenced to the vertex electrode (Cz). The montage did not include specific eye artifact channels, but did include channels for electrodes located above the eyes (FPz; FP1, FP2). Responses to target and non-target stimuli presented about every 2 s
Automatic artifact detection
Since we knew which data trials contained simulated artifacts, for each type of artifact, we could determine the most efficient artifact detection method. For each method, we chose one free parameter which we optimized to estimate an upper bound on the ability of the method to detect artifacts of the given type.
We assumed voltage thresholds to be symmetrical, so only one parameter had to be optimized in the standard thresholding method. Linear trend detection had two parameters (minimum slope
Results
Results for each detection method and each artifact type are presented in Fig. 2, which shows results for one artifact type in each row and one detection method in each column. Applied either to the raw data or to ICA component activities, the frequency thresholding method performed the best overall; the joint probability method was second best, and standard thresholding third. Kurtosis thresholding performed the poorest, though it was partly successful in detecting large discontinuity and
Discussion
We have shown that optimally applying spectral methods to identify artifacts in 32-channel EEG data epochs allowed more reliable detection of smaller artifacts than optimally applying the same thresholding methods to the scalp channel data itself. Preprocessing the data using ICA allows more effective artifact detection. However, it should be noted that the frequency thresholding methods are as efficient at detecting muscle artifact in either the raw or ICA-decomposed data. For this type of
Acknowledgments
This work was supported by a fellowship from the INRIA organization, by the Howard Hughes Foundation and the Swartz Foundation (Old Field, NY), and by the National Institutes of Health USA (grant RR13651-01A1).
References (23)
- et al.
Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals
Clin. Neurophysiol.
(2004) - et al.
EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis
J. Neurosci. Methods
(2004) - et al.
Interaction of top–down and bottom–up processing in the fast visual analysis of natural scenes
Brain Res. Cogn. Brain Res.
(2004) - et al.
Independent component analysis: algorithms and applications
Neural Netw.
(2000) - et al.
Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects
Clin. Neurophysiol.
(2000) - et al.
A unifying information-theoretic framework for independent component analysis
Comput. Math. Appl.
(2000) - et al.
Mining event-related brain dynamics
Trends Cogn. Sci.
(2004) - et al.
An information-maximization approach to blind separation and blind deconvolution
Neural Comput.
(1995) - et al.
Robust whitening procedure in blind source separation context
Electron. Lett.
(2000) - et al.
EEG changes accompanying learning regulation of the 12-Hz EEG activity
IEEE Trans. Rehabil. Eng.
(2003)
Cited by (1322)
An outlier detection based two-stage EEG artifact removal method using empirical wavelet transform and canonical correlation analysis
2024, Biomedical Signal Processing and ControlMulti-Algorithm Artifact Correction (MAAC) procedure part one: Algorithm and example
2024, Biological PsychologyThe search for the relationship between female hormonal status, alpha oscillations, and aperiodic features of resting state EEG
2024, International Journal of PsychophysiologyAtypical paroxysmal slow cortical activity in healthy adults: Relationship to age and cognitive performance
2024, Neurobiology of Aging