EEG SINGLE-TRIAL CLASSIFICATION OF VISUAL
ART PREFERENCE

Marc Welter, J Casal Martínez, E Redmond, Jonathan Baum, Tomas Ward,
Fabien Lotte

► To cite this version:

Marc Welter, J Casal Martínez, E Redmond, Jonathan Baum, Tomas Ward, et al.. EEG SINGLE-TRIAL CLASSIFICATION OF VISUAL ART PREFERENCE. Journées CORTICO 2024 - COllectif pour la Recherche Transdisciplinaire sur les Interfaces Cerveau-Ordinateur, May 2024, Nancy, France. hal-04579220

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Submitted on 17 May 2024

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EEG SINGLE-TRIAL CLASSIFICATION OF VISUAL
ART PREFERENCE

M. Welter¹, J. Casal Martínez², E. Redmond³, J. Baum¹, T. Ward³, F. Lotte¹

¹Inria Centre at the University of Bordeaux / LaBRI, France

² Valencia Polytechnic University, Spain

³ Dublin City University, Ireland

E-mail: marc.welter@inria.fr

Brain-Computer-Interfaces (BCIs) able to decode aesthetic preference could im-
prove user experience in digital spaces by personalizing aesthetic stimuli selection
without requiring explicit user feedback that might interrupt aesthetic experience. How-
ever, neuroscientific understanding of aesthetic experience remains lacking, while the
tried and tested BCI classification algorithms have not yet been applied to decode aes-
thetic preferences from Electroencephalography (EEG) signals. We thus conducted an
experiment in which 13 participants were exposed to 120 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 pref-
erence. 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 single-trial aesthetic pref-
erence classification problem. Artefacts were removed using Independent Component
Analysis. We report promising offline aesthetic preference decoding accuracies with
a mean of 0.798% ± 0.162 (see Figure 1). Despite the relatively high classification

<figure><img src="image_1.png" /><figcaption>Figure 1: Mean Aesthetic Preference Classification Scores For All Subjects</figcaption></figure>

performance, closing the gap towards online aesthetic preference decoding remains
challenging. Many BCIs require training on balanced data samples. However, it is
EEG SINGLE-TRIAL CLASSIFICATION OF VISUAL
ART PREFERENCE

M. Welter¹, J. Casal Martínez², E. Redmond³, J. Baum¹, T. Ward³, F. Lotte¹

¹Inria Centre at the University of Bordeaux / LaBRI, France

² Valencia Polytechnic University, Spain

³ Dublin City University, Ireland

E-mail: marc.welter@inria.fr

Brain-Computer-Interfaces (BCIs) able to decode aesthetic preference could im-
prove user experience in digital spaces by personalizing aesthetic stimuli selection
without requiring explicit user feedback that might interrupt aesthetic experience. How-
ever, neuroscientific understanding of aesthetic experience remains lacking, while the
tried and tested BCI classification algorithms have not yet been applied to decode aes-
thetic preferences from Electroencephalography (EEG) signals. We thus conducted an
experiment in which 13 participants were exposed to 120 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 pref-
erence. 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 single-trial aesthetic pref-
erence classification problem. Artefacts were removed using Independent Component
Analysis. We report promising offline aesthetic preference decoding accuracies with
a mean of 0.798% ± 0.162 (see Figure 1). Despite the relatively high classification

<figure><img src="image_2.png" /><figcaption>Figure 1: Mean Aesthetic Preference Classification Scores For All Subjects</figcaption></figure>

performance, closing the gap towards online aesthetic preference decoding remains
challenging. Many BCIs require training on balanced data samples. However, it is
often uncertain beforehand whether an individual will like an artwork of not. Objective properties of an artwork, such as symmetry or complexity, have been correlated with aesthetic ratings and could be used to estimate aesthetic preference a priori. In conclusion, aesthetic preference decoding BCIs seem feasible, but future work needs to investigate optimal training data selection during online calibration.
often uncertain beforehand whether an individual will like an artwork of not. Objective properties of an artwork, such as symmetry or complexity, have been correlated with aesthetic ratings and could be used to estimate aesthetic preference a priori. In conclusion, aesthetic preference decoding BCIs seem feasible, but future work needs to investigate optimal training data selection during online calibration.