Department of Psychology2024-11-0920169781-5090-1679-210.1109/SIU.2016.74962222-s2.0-84982814991http://dx.doi.org/10.1109/SIU.2016.7496222https://hdl.handle.net/20.500.14288/9930Bag-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.EngineeringElectrical and electronic engineeringConference proceedinghttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84982814991&doi=10.1109%2fSIU.2016.7496222&partnerID=40&md5=565630e1e98b3c57ba27d4884c02626d3912509005386519