A supervised sparse learning framework to solve EEG inverse problem for discriminative activations pattern

Abstract

Electroencephalography (EEG) is one of the most important noninvasive neuroimaging tools that provides excellent temporal accuracy. As the EEG electrode sensors measure electrical potentials on the scalp instead of direct measuring activities of brain voxels deep inside the head, many approaches are proposed to infer the activated brain regions due to its significance in neuroscience research and clinical application. However, since mostly part of the brain activity is composed of the spontaneous neural activities or non-task related activations, task related activation patterns will be corrupted in strong background signal/noises. In our research, we proposed a sparse learning framework for solving EEG inverse problem which aims to explicitly extract the discriminative sources for different cognitive tasks by fusing the label information into the inverse model. The proposed framework is capable of estimation the discriminative brain sources under given different brain states where traditional inverse methods failed. We introduced two models, one is formulated as supervised sparse dictionary learning and the other one is the graph regularized discriminative source estimation model to promote the consistency within same class. Preliminary experimental results also validated that the proposed sparse learning framework is effective to discover the discriminative taskrelated brain activation sources, which shows the potential to advance the high resolution EEG source analysis for real-time non-invasive brain imaging research.