Recognizing Special Art Pieces Through EEG: A Journey in Neuroaesthetics Classification

This paper describes three methods for analyzing electroencephalography (EEG) signals to classify users’ brain responses to exposure to art. The first two methods exploit classical machine learning approaches based on various sets of features extracted using different techniques for EEG analysis. In particular, the first method analyzes features extracted from time and frequency domains using an ensemble classifier, while the second one analyzes the Phase Locking Values of different channels using a classifier based on K-Nearest Neighbors. The third method retrains a well-known Convolutional Neural Network, namely VGG16, for image classification to analyze the scalograms obtained by applying the continuous wavelet transform to the EEG. These methods are evaluated employing a public dataset collected using mobile tools from museum visitors at an art exhibit. The dataset suffers from the problem of unbalanced classes, and this work also evaluates the impact of mitigation actions. The results reveal a significant difference between models tailored to the subject being tested, that achieve up to 95.43% of accuracy, and those trained without data from that subject (leave-one-subject-out strategy), that achieve up to 65.35% of accuracy. This suggests that, at this stage, customized approaches are more appropriate for the neuroaesthetics field, whereas models with general applicability need further development. We also conducted an analysis to assess whether age influenced the performance of the models.We split the visitors into three groups based on their age: adolescents, young adults, and adults. The analysis performed with a leave-one-subject-out strategy revealed higher accuracy for adolescents (73.21%) and adults (66.98%) than young adults (60%).