In this study, we propose an approach to identify individuality that appears in human brain dynamics and visualize inter-individual variations in a low-dimensional space. For this purpose, we first introduce an appropriate similarity measure between multichannel electroencephalography (EEG) signals based on information geometry. Then, we use t-distributed stochastic neighbor embedding, which is a state-of-the-art algorithm for manifold learning, and combine it with the information distance to map points in the high-dimensional EEG signal space into two-dimensional space. We show that a fine low-dimensional visualization enables us to identify each subject as an isolated island of points and preserve inter-individual variations. We also provide an appropriate approach to tune the cost function parameter.
CITATION STYLE
Suetani, H., Mizuno, Y., & Kitajo, K. (2018). A Manifold Learning Approach to Chart Human Brain Dynamics Using Resting EEG Signals. In Springer Proceedings in Complexity (pp. 359–367). Springer. https://doi.org/10.1007/978-3-319-96661-8_37
Mendeley helps you to discover research relevant for your work.