  Functional MRI (fMRI) is widely used to examine brain functionality by
detecting alteration in oxygenated blood flow that arises with brain activity.
In this study, complexity specific image categorization across different visual
datasets is performed using fMRI time series (TS) to understand differences in
neuronal activities related to vision. Publicly available BOLD5000 dataset is
used for this purpose, containing fMRI scans while viewing 5254 images of
diverse categories, drawn from three standard computer vision datasets: COCO,
ImageNet and SUN. To understand vision, it is important to study how brain
functions while looking at different images. To achieve this, spatial encoding
of fMRI BOLD TS has been performed that uses classical Gramian Angular Field
(GAF) and Markov Transition Field (MTF) to obtain 2D BOLD TS, representing
images of COCO, Imagenet and SUN. For classification, individual GAF and MTF
features are fed into regular CNN. Subsequently, parallel CNN model is employed
that uses combined 2D features for classifying images across COCO, Imagenet and
SUN. The result of 2D CNN models is also compared with 1D LSTM and Bi-LSTM that
utilizes raw fMRI BOLD signal for classification. It is seen that parallel CNN
model outperforms other network models with an improvement of 7% for
multi-class classification. Clinical relevance- The obtained result of this
analysis establishes a baseline in studying how differently human brain
functions while looking at images of diverse complexities.
