Computational neuroscienceInformed decomposition of electroencephalographic data

- BSS techniques have become the standard for decomposing EEG data but are not optimal when additional information is known about the problem.
- We propose Informed Multidimensional ICA (IMICA) that builds on ISA and ISS techniques to better model distinct subspaces within EEG data.
- We show that IMICA outperforms other common methods such as Infomax ICA, FastICA, PCA, JADE, and SOBI.
- The results show that IMICA better isolates and removes subspaces within the EEG data.

BackgroundBlind source separation techniques have become the de facto standard for decomposing electroencephalographic (EEG) data. These methods are poorly suited for incorporating prior information into the decomposition process. While alternative techniques to this problem, such as the use of constrained optimization techniques, have been proposed, these alternative techniques tend to only minimally satisfy the prior constraints. In addition, the experimenter must preset a number of parameters describing both this minimal limit as well as the size of the target subspaces.New methodWe propose an informed decomposition approach that builds upon the constrained optimization approaches for independent components analysis to better model and separate distinct subspaces within EEG data. We use a likelihood function to adaptively determine the optimal model size for each target subspace.ResultsUsing our method we are able to produce ordered independent subspaces that exhibit less residual mixing than those obtained with other methods. The results show an improvement in modeling specific features of the EEG space, while also showing a simultaneous reduction in the number of components needed for each model.Comparison with existing method(s)We first compare our approach to common methods in the field of EEG decomposition, such as Infomax, FastICA, PCA, JADE, and SOBI for the task of modeling and removing both EOG and EMG artifacts. We then demonstrate the utility of our approach for the more complex problem of modeling neural activity.ConclusionsBy working in a one-size-fits-all fashion current EEG decomposition methods do not adapt to the specifics of each data set and are not well designed to incorporate additional information about the decomposition problem. However, by adding specific information about the problem to the decomposition task, we improve the identification and separation of distinct subspaces within the original data and show better preservation of the remaining data.