DEEP LEARNING FOR BRAIN DECODING

Date

2014-10-30

Author

Firat, Orhan
GİLLAM, İLKE
Yarman Vural, Fatoş Tunay

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Deep learning, Stacked autoencoders, fMRI, MVPA, Brain state decoding

URI

https://hdl.handle.net/11511/55909

Conference Name

IEEE International Conference on Image Processing (ICIP)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

O. Firat, İ. GİLLAM, and F. T. Yarman Vural, “DEEP LEARNING FOR BRAIN DECODING,” presented at the IEEE International Conference on Image Processing (ICIP), Paris, FRANCE, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55909.