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 predefined 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.