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Stimulus decoding of functional Magnetic Resonance Imaging (fMRI) data withmachine learning models has provided new insights about neural representationalspaces and task-related dynamics. However, the scarcity of labelled(task-related) fMRI data is a persistent obstacle, resulting inmodel-underfitting and poor generalization. In this work, we mitigated datapoverty by extending a recent pattern-encoding strategy from the visual memorydomain to our own domain of auditory pitch tasks, which to our knowledge hadnot been done. Specifically, extracting preliminary information aboutparticipants' neural activation dynamics from the unlabelled fMRI data resultedin improved downstream classifier performance when decoding heard and imaginedpitch. Our results demonstrate the benefits of leveraging unlabelled fMRI dataagainst data poverty for decoding pitch based tasks, and yields novelsignificant evidence for both separate and overlapping pathways of heard andimagined pitch processing, deepening our understanding of auditory cognitiveneuroscience.

Decoding Imagined Auditory Pitch Phenomena with an Autoencoder Based Temporal Convolutional Architecture