Data Augmentation for Neuroaesthetics Analysis

Neuroaesthetics investigates the neural activities during aesthetic experiences, using EEG recordings or fMRI images to decode the perception of visual art. However, studies in this domain are hindered by the limited and imbalanced nature of datasets, which is due to the subjective and resource-intensive nature of data collection. This study examines the effectiveness of various data augmentation strategies in enhancing EEG classification performance for neuroaesthetic analysis. We experiment three different EEG augmentation techniques, namely Signal Segmentation and Recombination, Temporal and Spatial Reconstruction Data Augmentation, and Gaussian Noise Addition. Furthermore, once extracted the features from the signals, we applied an instance-level data augmentation algorithm, namely SMOTE. We tested the four augmentation techniques individually, as well as SMOTE applied in cascade with the three EEG-specific augmentation methods, using stratified ten-fold cross-validation and leave-one-subject-out validation strategies. Results show that Gaussian Noise Addition, particularly when combined with SMOTE for generalization, yields consistent performance improvements in both accuracy and F-score. Conversely, Temporal and Spatial Reconstruction Data Augmentation often degrades classification performance (up to −9.95% of accuracy). Finally, Signal Segmentation and Recombination achieved the best improvement in the leave-one-subject-out analysis (+2.2% accuracy). Our findings present that appropriate data augmentation can enhance model generalization for aesthetic experience classification.