Current EEG/MEG based neuroimaging technologies receive limited attentions in functional neuroimaging because their reliable performances have not been successfully demonstrated in sophisticated brain tasks. The need for more reliable, efficient, innovative EEG/MEG neuroimaging technologies that have high spatial and temporal resolutions is urgent. The goal of proposed research is to advance functional neuroimaging technologies in studying human brain functions, via integrating novel techniques from different disciplines, i.e. multi-resolution mathematic models, large-scale computation, advanced signal and image processing, and high-end measurement devices.
Intellectual Merit:
The proposed research consists of applying the well-established L-1 norm regularization technique to a different application domain, namely EEG/MEG image analysis. The problem of image analysis is an undetermined problem, and as such some form of regularization is required. The traditional approach is to apply L-2 norm minimization, which is a valid method when measurement errors are Gaussian random variables. However, when the measurement errors are exponentially distributed, it is more meaningful to use L-1 norm minimization. Moreover, the L-1 optimal solution is supported on a finite (usually small) number of data points, thus naturally taking advantage of the sparse nature of the optimization problem. The novelty of the proposed research is the investigation of sparsity inducing models of neural behavior. In the case of neuro-imaging, it is known from biological factors that during any one millisecond interval, only a tiny fraction of the neurons in the brain "fire". The proposed research therefore has the potential to identify the parameters that describe this behavior. As partial evidence of this, the PI has obtained some preliminary results based on synthetic data, when the correct answer is known, and the results are encouraging.
Broader Impact:
The project will generate novel problem-solving strategies for scientific problems using advanced engineering principles, and to clinical practice for neurological patients through the potential for reduction in loss of life, job, and income. By incorporating authentic learning education activities, we will broaden the participation of all levels of students, K-12 teachers, and other educators, in biomedical research and research educations.
