Machine learning approach for classifying the cognitive states of the human brain with functional magnetic resonance imaging (fMRI)

Abstract

Cognitive state classification is a challenging task. Many studies were reported using different neuroimaging modalities for classification of the cognitive states of the human brain e.g., EEG, fMRI, MEG etc. However, functional MRI seems to be appropriate for these papers as due to its good spatial resolution and localizing the brain activated regions. In this paper, our objective is to identify the different cognitive brain states. For example, classifying the patterns of high and low cognitive loads. We acquired the fMRI data on the healthy participants. First, data is preprocessed to remove the artifacts and motions corrections. Next, regions of interest were extracted from functional brain volumes of the two states. Data reduction is also performed and data were passed to machine learning classifier i.e., support vector machine. The results showed that high and low cognitive loads were successfully classified with good accuracy.