Identifying reliable independent components via split-half comparisons

  title={Identifying reliable independent components via split-half comparisons},
  author={David M. Groppe and Scott Makeig and Marta Kutas},
  volume={45 4},
Independent component analysis (ICA) is a family of unsupervised learning algorithms that have proven useful for the analysis of the electroencephalogram (EEG) and magnetoencephalogram (MEG). ICA decomposes an EEG/MEG data set into a basis of maximally temporally independent components (ICs) that are learned from the data. As with any statistic, a concern with using ICA is the degree to which the estimated ICs are reliable. An IC may not be reliable if ICA was trained on insufficient data, if… CONTINUE READING