One of the core goals in the field of cognitive neuroscience is to decode task state fMRI data. Task decoding is the process of taking neuroimaging data and determining the task that was performed when that data was collected. A large volume of work used for task decoding is done in pursuit of creating a deep learning model for task prediction. Typically these models will include either handcrafted features or data driven approaches for downscaling the input features in successive layers. In this thesis, we explore and compare the effectiveness of linear, graph-based and attention-based methods for hierarchical classification. Furthermore, we propose a new attention-based network architecture which showcases superior performance to all of our baseline architectures without the use of handcrafted features on several neuroimaging datasets.
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