DEEP LEARNING FOR BRAIN DECODING


Firat O., GİLLAM İ., YARMAN VURAL F. T.

IEEE International Conference on Image Processing (ICIP), Paris, France, 27 - 30 October 2014, pp.2784-2788 identifier

  • Publication Type: Conference Paper / Full Text
  • City: Paris
  • Country: France
  • Page Numbers: pp.2784-2788

Abstract

Learning low dimensional embedding spaces (manifolds) for efficient feature representation is crucial for complex and high dimensional input spaces. Functional magnetic resonance imaging (fMRI) produces high dimensional input data and with a less then ideal number of labeled samples for a classification task. In this study, we explore deep learning methods for fMRI classification tasks in order to reduce dimensions of feature space, along with improving classification performance for brain decoding. We employ sparse autoencoders for unsupervised feature learning, leveraging unlabeled fMRI data to learn efficient, non-linear representations as the building blocks of a deep learning architecture by stacking them. Proposed method is tested on a memory encoding/retrieval experiment with ten classes. The results support the efficiency compared to the baseline multi-voxel pattern analysis techniques.