SPM. They can be considered more “principled” than the above approach in that the whole data can be included (without artificially reducing the data) while correcting for multiple comparisons using methods that take into account non-independence in the EEG data (and that are therefore more sensitive than overly-conservative Bonferroni corrections). A further example is Fieldtrip cluster-based statistics; these are non-parametric tests applied to all electrodes/time-points of interest. Non-parametric tests do not make assumptions about the distribution of the data (i.e. not required to be normally distributed) but have the disadvantage of providing less complex models (e.g. complex ANOVAs are not possible – tests are restricted to single factors). This can be partially circumvented by conducting subtractions first, e.g. subtracting activity from target vs. non-target trials, prior to conducting paired tests on orthogonal conditions of interest trials (which approximates an interaction effect). However, this method is currently only available for sensor-level data and not source-level data. Alternatively, SPM sensor and source statistics conducts parametric statistics, allowing complex ANOVAs, and can be applied to sensor and source data. However, it assumes normality of the residuals of the model (which may not be accurate for some types of data).
Multivariate methods. The multiple comparisons problem, and the problem of assuming certain data distributions (i.e. normality), can be bypassed using multivariate approaches such as multivariate pattern analysis (MVPA). These analyses look for co-dependent patterns across the data (e.g. over electrodes and time) rather than differences in mean levels of activity. MVPA is particularly sensitive to differences between conditions compared to univariate methods and therefore provides higher statistical power. Such methods are commonly used for classification (i.e. using neural activity to predict class membership, e.g. classifying a patient vs. healthy control, or classifying one condition from another). However, the results are less easy to interpret as they do not necessarily localise the neural activity to specific points in space and time. There are methods that can around this by applying sub-sections of the data at a time to the classification algorithm (e.g. searchlight methods).