Dimensionality Reduction Of Brain Imaging Data Using Graph Signal Processing

Brain imaging data such as EEG or MEG is high-dimensional spatiotemporal measurements that commonly require dimensionality reduction before being used for further analysis or applications. This paper presents a new dimensionality reduction method based on the recent graph signal processing theory. Specifically, we focus on a task to classify the brain imaging signals recording the cortical activities in response to visual stimuli. We propose to use the resting-state measurements (i.e., before onset of the stimulus) of the subjects to build a connectivity graph. The graph Laplacian and Graph-Based Filtering (GBF) are then applied to learn the low-dimensional linear subspace for the task-state measurements (i.e., after onset of the stimulus). We investigate different techniques to build the connectivity graph suitable for this application. Compared with other dimensionality reduction methods such as Principle Component Analysis (PCA), GBF-based dimensionality reduction leverages the connectivity graph as side information to analyze the degraded, non Gaussian noise corrupted measurements. Experimental results with synthetic and real MEG datasets are presented.