<p>Understanding the neural representation of concrete nouns has transformative potential in psychology, neuroscience, linguistics, and computer science. Feature selection plays a crucial role in improving the classification accuracy of functional magnetic resonance imaging (fMRI)-based semantic decoding. This study delves into how the human brain encodes these nouns by employing cutting-edge feature selection techniques to classify brain activation patterns with enhanced precision. An innovative cascaded feature selection method is presented, integrating variance thresholding and Principal Component Analysis to effectively reduce dimensionality and boost classification accuracy. Using fMRI data, the method was applied to brain scans of participants engaged with concrete noun-picture pairs. The results reveal that this cascaded feature selection approach markedly enhances the accuracy of identifying brain activation patterns linked to concrete nouns, offering a powerful tool for decoding semantic structures in the human cortex. This work underscores the efficacy of combining multiple feature selection techniques to advance neural data analysis and deepen the understanding of neurosemantic representations.</p>