able to decode aesthetic preference could improve user experience in digital spaces by personalizing aesthetic stimuli selection without requiring explicit user feedback that might interrupt aesthetic experience. However, neuroscientific understanding of aesthetic experience remains lacking, while the tried and tested BCI classification algorithms

have not yet been applied to decode aesthetic preferences from EEG signals. We thus conducted an experiment in which participants where exposed to visual artworks in a virtual museum and requested to grade their preferences for each of them, all this while their EEG was being measured. Previous neuroaesthetic research suggested that oscillatory modulations in different neural frequency bands could be informative of aesthetic preference. Therefore, we tested a time-frequency feature classification method widely used in BCIs, i.e. Filterbank Common Spatial Patterns feature extraction together with shrinkage Linear Discriminant Analysis, in a 2-class aesthetic preference classification problem. We report promising aesthetic preference decoding accuracies significantly and substantially above chance level.