A manifold learning approach to mapping individuality of human brain oscillations through beta-divergence.

This paper proposes an approach for visualizing individuality and inter-individual variations of human brain oscillations measured as multichannel electroencephalographic (EEG) signals in a low-dimensional space based on manifold learning. Using a unified divergence measure between spectral densities termed the "beta-divergence", we introduce an appropriate dissimilarity measure between multichannel EEG signals. Then, t-distributed stochastic neighbor embedding (t-SNE; a state-of-the-art algorithm for manifold learning) together with the beta-divergence based distance was applied to resting state EEG signals recorded from 100 healthy subjects. We were able to obtain a fine low-dimensional visualization that enabled each subject to be identified as an isolated point cloud and that represented inter-individual variations as the relationships between such point clouds. Furthermore, we also discuss how the performance of the low-dimensional visualization depends on the beta-divergence parameter and the t-SNE hyper parameter. Finally, borrowing from the concept of locally linear embedding (LLE), we propose a method for projecting the test sample to the t-SNE space obtained from the training samples and investigate that availability.