ABSTRACT
Recent advances on deep neural networks, especially in combination with generative models, provide the means to deal with the large amounts of data and noisiness in the neuroimaging domain. This chapter provides an overview of state-of-the-art techniques to learn complex features from brain signal recordings that capture processes underlying auditory imagery. An introduction to important deep learning techniques and their application to auditory and neuroimaging data are given. Special attention is paid to generative models and reverse inference. These models allow to build new research paradigms, such as stimulus reconstruction from multiple sensory modalities. We discuss how models trained on sensory data can be used for inference about imagery processes, which are not directly associated with the auditory stimulus and thus make complex demands to machine learning models that traditionally require large amounts of annotated data. Deep neural networks generally require large amounts of training data, while the amount and size of available datasets are small. This motivates a description of requirements for new datasets in the field. Based on these considerations, a final section discusses possible future improvements for deep neural networks in the auditory imagery domain, such as biologically inspired computational models.
