EEGIFT: Group Independent Component Analysis for Event-Related EEG Data
Figure 1
Group ICA. In the group ICA model, we assume that the EEG is a linear mixture of temporally independent sources in each subject . The linear combination of sources is represented by the unknown mixing matrix , and yields the ideal samples of brain activity u(t), and the signals recorded with the EEG amplifier . Transformations during preprocessing contain filtering, epoching, artefact rejection, individual ICA for additional artefact reduction and so forth, altering the effective temporal sampling and dimensionality of the data . For each individual separately, the pre-processed single trial data are pre-whitened and reduced to via PCA. Group data is generated by concatenating individual principal components in the aggregate data set . Temporal ICA is performed in this set, estimating aggregate components . From the aggregate components, the individual data are reconstructed (see text for details).