Modelling approach and avoidance behaviour : A deep learning approach to understand the human olfactory system

Abstract: In this thesis we examine the question whether it is possible to model approach and avoidance behaviour with probabilistic machine learning. The results from this project will primarily aid in our collective understanding of human existence. Secondly, it will extend the knowledge with regards to probabilistic machine learning in the Neuroscience domain. We aid this through building a Variational Recurrent Neural Network (VRNN) that is trained on Electroencephalography (EEG)-data from participants that is subjected to odours with varying pleasantness. The pleasantness of the odours is used to divide the participants into two classes based on their self reported experience. This data is used to train the VRNN. The performance of the VRNN is evaluated by how well we are able to reconstruct the original data from a low dimensional latent representation. In this task the model performs on a similar level as related works. We further investigate how changes in the latent space effects reconstructed data. Despite being disentangled, the latent variables are hard to interpret. Furthermore we try to classify and cluster the latent space as either approach or avoidance behaviour with a Support Vector Machine and Uniform Manifold Approximation. The classification results are only slightly better than random, indicating that the learned latent space is not suitable for the task This is most likely due to the patterns that make up approach and avoidance behaviour is seen as noise by the VRNN. This leads to the patterns not being accurately modelled. This is shown by the evidence that frontal α -asymmetry that exists in the data is not reconstructed by the model. The conclusion is therefore that a VRNN is less suitable for modelling underlying behaviour from raw EEG data due to the low signal to noise ratio. We instead suggests to focus on specific frequency ranges in specific regions when applying machine learning in this domain.