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Affirmative and Negative Sentence Detection in the Brain Using SVM-RFE and Rotation Forest: An fMRI Study

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Abstract

Studies on the brain’s response to positive and negative stimuli using non-invasive techniques such as fMRI and PET scans have been widely researched. The use of fMRI in particular, to study sentence polarity detection in the brain, has become a captivating field. This paper analyzed fMRI data of affirmative and negative sentence processing and developed a model using a correlation-based subset evaluator and NBTree classification to classify sentence polarity with an average accuracy of 92.9%. Further, the accuracy of the result is enhanced up to 100% using SVM-RFE and Rotation Forest. Our analysis of selected voxel sets in the brain showed that the calcarine sulcus and right and left dorsolateral prefrontal cortices play a significant role in determining sentence polarity, while areas such as the right posterior pre-central sulcus, right supramarginal gyrus, and right frontal eye fields have a limited contribution.

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Data Availability

Thedataset analysed during the current study are available in the public repository, and the link for the same is: http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-81/www/.

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Authors and Affiliations

  1. VIT Bhopal University, Sehore, India

    Ashish Ranjan

  2. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India

    Vibhav Prakash Singh

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  1. Ashish Ranjan
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This article is part of the topical collection “Machine Intelligence and Smart Systems guest” edited by Manish Gupta and Shikha Agrawal.

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Ranjan, A., Singh, V.P. Affirmative and Negative Sentence Detection in the Brain Using SVM-RFE and Rotation Forest: An fMRI Study. SN COMPUT. SCI. 4, 332 (2023). https://doi.org/10.1007/s42979-023-01786-1

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