  Advances in computational cognitive neuroimaging research are related to the
availability of large amounts of labeled brain imaging data, but such data are
scarce and expensive to generate. While powerful data generation mechanisms,
such as Generative Adversarial Networks (GANs), have been designed in the last
decade for computer vision, such improvements have not yet carried over to
brain imaging. A likely reason is that GANs training is ill-suited to the
noisy, high-dimensional and small-sample data available in functional
neuroimaging. In this paper, we introduce Conditional Independent Components
Analysis (Conditional ICA): a fast functional Magnetic Resonance Imaging (fMRI)
data augmentation technique, that leverages abundant resting-state data to
create images by sampling from an ICA decomposition. We then propose a
mechanism to condition the generator on classes observed with few samples. We
first show that the generative mechanism is successful at synthesizing data
indistinguishable from observations, and that it yields gains in classification
accuracy in brain decoding problems. In particular it outperforms GANs while
being much easier to optimize and interpret. Lastly, Conditional ICA enhances
classification accuracy in eight datasets without further parameters tuning.
