In contrast to block and event-related designs for fMRI experiments, it becomes much more difficult to extract events of interest in the complex continuous stimulus for finding corresponding blood-oxygen-level dependent (BOLD) responses. Recently, in a free music listening fMRI experiment, acoustic features of the naturalistic music stimulus were first extracted, and then principal component analysis (PCA) was applied to select the features of interest acting as the stimulus sequences. For feature generation, kernel PCA has shown its superiority over PCA in various applications, since it can implicitly exploit nonlinear relationship among features and such relationship seems to exist generally. Here, we applied kernel PCA to select the musical features and obtained an interesting new musical feature in contrast to PCA features. With the new feature, we found similar fMRI results compared with those by PCA features, indicating that kernel PCA assists to capture more properties of the naturalistic music stimulus.