Improvement of temporal resolution of fMRI data for brain decoding

Varol, Emel
In this study, we aim to increase the accuracy of the mapping between the states of the brain and problem-solving phases namely planning and execution. To create a computational model to generate the mapping, an fMRI dataset obtained from subjects solving the Tower of London problem has been used. fMRI data is suitable for this problem as it provides regional and time-varying changes in brain metabolism. However, developing the model using fMRI data is not trivial. Generally, fMRI data has a very large feature vector while having a small sample size due to the scanner limitations. We propose two methods to overcome these limitations and increase the mapping performance. Both methods have a preliminary stage where we perform preprocessing. Preprocessing stage includes feature selection and whitening. The proposed methods are built with polynomial regression and neural networks utilizing the spatial and temporal nature of the data.


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Citation Formats
E. Varol, “Improvement of temporal resolution of fMRI data for brain decoding,” M.S. - Master of Science, Middle East Technical University, 2022.