Supervised discriminative EEG brain source imaging with graph regularization

Abstract

As Electroencephalography (EEG) is a non-invasive brain imaging technique that records the electric field on the scalp instead of direct measuring activities of brain voxels on the cortex, many approaches were proposed to estimate the activated sources due to its significance in neuroscience research and clinical applications. However, since most part of the brain activity is composed of the spontaneous neural activities or non-task related activations, true task relevant activation sources can be very challenging to be discovered given strong background signals. For decades, the EEG source imaging problem was solved in an unsupervised way without taking into consideration the label information that representing different brain states (e.g. happiness, sadness, and surprise). A novel model for solving EEG inverse problem called Graph Regularized Discriminative Source Imaging (GRDSI) was proposed, which aims to explicitly extract the discriminative sources by implicitly coding the label information into the graph regularization term. The proposed model is capable of estimating the discriminative brain sources under different brain states and encouraging intra-class consistency. Simulation results show the effectiveness of our proposed framework in retrieving the discriminative sources.