Classification of categorical objects in ventral temporal cortex using fMRI data

Abstract

Functional Magnetic Resonance Imaging(fMRI) is one of the best neuroimaging techniques which helps to understand the activity of the human brain. With the help of recent advancement in the field of machine learning algorithms in terms of pattern recognition, now it is possible to extract in-depth information about brain activity by analyzing fMRI data. In this paper, we have shown the analysis of the data of a particular part of the human brain called Ventral Temporal Cortex. The dataset contains the fMRI data of the subjects while viewing grey-scale image different categories of objects such as cat, chair, etc. We have applied the machine learning algorithms on the extracted feature set from fMRI data to classify the objects that the subject is viewing. Here, we have emphasized on hyper-parameter tuning for the classifiers. Among the classifiers, we have found that the performance of Support Vector Machine (i.e., 96.92%) and k-nearest neighbor classifier(i.e., 96.90%) is quite persistent and have better accuracy. The further application of this research may motivate to develop brain-computer interface (BCI) based solutions.