The Effects of Acute Moderate Exercise on Cognitive Functioning — Part 5
Analysis Methodology 2: ICA & Average ERP Waveforms
A common technique for removing artifacts is to reject epochs that contained artifacts (Li & Principe, 2006). However, the prevalence of artifacts in a given block of trials may be so high that rejecting all of the contaminated epochs may be unrealistic (Li & Principe, 2006). Furthermore, removing epochs containing artifacts may reduce the statistical power for the analysis (Mennes et al., 2010). In response to this issue, researchers have devised other methods for removing artifacts (Li & Principe, 2006). Independent component analysis (ICA) has been put forward as a strategy to effectively remove a variety of artifacts commonly found in ERP (Li & Principe, 2006).
ICA is a method aimed to linearly separate data so that the data is uncorrelated (Li & Principe, 2006; Groppe et al., 2008). ICA makes assumptions about the underlying neural generators of ERPs (Li & Principe, 2006; Groppe et al., 2008): 1) the neural generators are temporally independent and non-Gaussian; and 2) the number of independent sources is equal to the number of electrodes (Groppe et al., 2008). Previous research suggests that these assumptions are somewhat valid and that ICA appears to extract EEG artifacts successfully (Groppe et al., 2008). Support for ICA comes from a comparison conducted between a regression method (eye movement correction procedure (EMCP) and ICA (Hoffman & Falkenstein, 2008). The results of this comparison on the removal of eye blink artifacts from real and simulated data concluded that ICA yields an almost perfect correction, whereas the accuracy of the EMCP correction depends on the variant used and the structure of the data (Hoffman & Falkenstein, 2008). Research assessing the validity of ICA-based methods indicates that removal of eye movement artifacts from ERPs using the extended Infomax ICA algorithm produced the greatest reduction of eye blink artifacts and is preferable to other algorithms available on EEGLAB (Pontifex et al., 2017). When using ICA, component maps of the independent components (ICs) depict distinct scalp topographies for blinks and ERP components, making it possible to separate the two sources (Li & Principe, 2006). Moreover, the ICs generated seem to have a plausible physiological basis (Groppe et al., 2008). This study will employ ICA prior to additional processing, descried below.
- Load in the artifact removed ERP data for each individual
2. Organize the data and average across participant, condition, time, and flanker task
3. Plot one individual’s average ERP waveform across conditions, times, and flankers
MCT here refers to the moderate exercise condition
1 & 2 refer to the congruent and incongruent flanker conditions respectively
4. Plotting the same individual’s data with a smaller time window
5. Plotting the grand average ERP waveform
