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.

认知状态分析或阅读大脑始终是研究人员一个令人兴奋的领域。在人从事特定任务时对人脑的分析是神经成像研究的最新发展中必不可少的主题。引入了诸如PET（正电子发射断层扫描）和fMRI（功能磁共振成像）等新的非侵入性方法来分析大脑。同时，受试者参与各种活动。这项研究旨在调查大脑中肯定和否定句子的处理。使用基于贪婪逐步相关的特征选择技术和随机森林分类方法，我们的模型可以对句子极性检测任务中的认知状态进行分类，平均准确度为95.41％。我们还分析了类别特定的选定特征体素集，以确定大脑中句子的极性。我们的结果表明，CALC，RDLPFC和LDLPFC在区域特征选择方面发挥了积极作用。相反，RPPREC，RSGA，RFEF对极性检查的添加很少。