SRC, an acronym for Stimulus-response correlation, refers to determining the relationship between stimulus and corresponding brain responses. The neural aesthetic resonance hypothesis proposes that the level of enjoyment or familiarity can be distinguishable based on the relationship between stimulus and brain responses. To test this hypothesis, we use EEG data of 20 participants listening to 12 songs with their enjoyment and familiarity ratings. We aim to classify the low and high ratings of familiarity and enjoyment based on SRC. Eighteen musical features are extracted and transformed into the first principal component (PC1). In addition, root mean square (RMS) and spectral flux are used for analysis. Canonical Correlation Analysis (CCA), an unsupervised AI optimization method, is employed to compute the SRC between musical features and ten regions of brain responses, followed by considering four principal CCA features for classification using the Random Forest classifier with cross-subject evaluation. Our results demonstrate that the right frontal and right parietal regions provide significant predictive ability. Our empirical finding suggests that RMS features preserve the predictive ability for familiarity, whereas PC1 is for enjoyment prediction. Maximum familiarity and enjoyment accuracy reach nearly 76% and 73% accuracy. This work leverages AI techniques to decode sensor-derived neural signals, advancing real-time applications in affective computing and wearable EEG devices.