Sentence polarity detection using stepwise greedy correlation based feature selection and random forests: An fMRI study

Abstract

Cognitive state analysis or reading the brain was always an exciting field for researchers. Analysis of the human brain while a person is engaged in doing a particular task is an essential topic in the recent development of neuro-imaging studies. The introduction of new non-invasive methods like PET (Positron Emission Tomography) and fMRI (Functional Magnetic Resonance Imaging) is used to analyze the brain. At the same time, subjects are involved in doing diverse activities. This study aims to investigate the processing of affirmative and negative sentences in the brain. Using a greedy stepwise correlation-based feature selection technique and random forest classification approach, our model can classify the cognitive state in sentence polarity detection task with, on average, 95.41% accuracy. We have also analyzed the category-specific selected feature voxel set in determining the sentence polarity in the brain. Our result shows that CALC, RDLPFC, and LDLPFC are positively contributing areas feature selection. In contrast, RPPREC, RSGA, RFEF add very little to polarity check. © 2021 Elsevier Ltd