  Functional magnetic resonance imaging produces high dimensional data, with a
less then ideal number of labelled samples for brain decoding tasks (predicting
brain states). In this study, we propose a new deep temporal convolutional
neural network architecture with spatial pooling for brain decoding which aims
to reduce dimensionality of feature space along with improved classification
performance. Temporal representations (filters) for each layer of the
convolutional model are learned by leveraging unlabelled fMRI data in an
unsupervised fashion with regularized autoencoders. Learned temporal
representations in multiple levels capture the regularities in the temporal
domain and are observed to be a rich bank of activation patterns which also
exhibit similarities to the actual hemodynamic responses. Further, spatial
pooling layers in the convolutional architecture reduce the dimensionality
without losing excessive information. By employing the proposed temporal
convolutional architecture with spatial pooling, raw input fMRI data is mapped
to a non-linear, highly-expressive and low-dimensional feature space where the
final classification is conducted. In addition, we propose a simple heuristic
approach for hyper-parameter tuning when no validation data is available.
Proposed method is tested on a ten class recognition memory experiment with
nine subjects. The results support the efficiency and potential of the proposed
model, compared to the baseline multi-voxel pattern analysis techniques.

Machine Learning;Neural and Evolutionary Computing