The algorithm developed in this study integrates a frequency analysis of key frequency bands (Alpha, Beta, Delta, and Theta) with the principal component analysis (PCA) in order to validate brain functional mappings associated with the characterization effects of an Auditory/Comprehension task. This study provides added insight to earlier findings involving the Wernicke and Broca's brain areas in relation to language comprehension. A thorough examination of the electroencephalograph (EEG) recordings through the PCA reveals that eigenvectors associated with the largest eigenvalues produce an interesting activity pattern directly attributable to those characteristic behaviors found in the Alpha, Beta, Delta, and Theta frequency bands. The clinical EEG data involved 9 patients at Miami Children's Hospital using the Electrical Source Imaging system with 256 electrodes. An evaluation of spectral arrays is performed using topographic maps of the induced brain activities during both listening and answering phases. This evaluation is then augmented with quantifying measures using the PCA while results are validated through integration of EEG and PCA modalities. Such a representation allows us to bring new insight out on how different patients react under different circumstances, and be able to detect consequently the presence of potential neurological disorders by assessing similar/dissimilar behaviors with respects to all former patients already included in the database. The good results obtained are foreseen to extend the algorithm's application to other brain functional mapping tasks.