Decoding Cognitive States Using the Bag of Words Model on fMRI Time Series

Sucu, Güneş
Akbaş, Emre
Oztekin, Ilke
Mizrak, Eda
Yarman Vural, Fatoş Tunay
Bag-of-words (BoW) modeling has yielded successful results in document and image classification tasks. In this paper, we explore the use of BoW for cognitive state classification. We estimate a set of common patterns embedded in the fMRI time series recorded in three dimensional voxel coordinates by clustering the BOLD responses. We use these common patterns, called the code-words, to encode activities of both individual voxels and group of voxels, and obtain a BoW representation on which we train linear classifiers. Our experimental results show that the BoW encoding, when applied to individual voxels, significantly improves the classification accuracy (an average 7.2% increase over 13 different datasets) compared to a classical multi voxel pattern analysis method. This preliminary result gives us a clue to generate a code-book for fMRI data which may be used to represent a variety of cognitive states to study the human brain.
Citation Formats
G. Sucu, E. Akbaş, I. Oztekin, E. Mizrak, and F. T. Yarman Vural, “Decoding Cognitive States Using the Bag of Words Model on fMRI Time Series,” presented at the 24th Signal Processing and Communication Application Conference (SIU), 2016, Accessed: 00, 2020. [Online]. Available: