1998/06/16 by David Schmidt, David M. Schmidt, John George +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Blind Source Separation Techniques #FOS: Biological sciences #Image and Signal Denoising Methods #Neurons and Cognition (q-bio.NC) #Quantitative Methods (q-bio.QM) #Statistical and numerical algorithms #q-bio.NC #q-bio.QM
paper · pdf · doi:10.48550/arxiv.q-bio/0309029
11 pages, 16 figures, pdf, submitted to Human Brain Mapping, color figures at http://stella.lanl.gov/bi.html. Originally submitted to the neuro-sys archive which was never publicly announced (was 9806001)
arxiv created 1998/06/16 · openalex publication_date 1998/06/16 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a new approach to the electromagnetic inverse problem that explicitly addresses the ambiguity associated with its ill-posed character. Rather than calculating a single ``best'' solution according to some criterion, our approach produces a large number of likely solutions that both fit the data and any prior information that is used. While the range of the different likely results is representative of the ambiguity in the inverse problem even with prior information present, features that are common across a large number of the different solutions can be identified and are associated with a high degree of probability. This approach is implemented and quantified within the formalism of Bayesian inference which combines prior information with that from measurement in a common framework using a single measure. To demonstrate this approach, a general neural activation model is constructed that includes a variable number of extended regions of activation and can incorporate a great deal of prior information on neural current such as information on location, orientation, strength and spatial smoothness. Taken together, this activation model and the Bayesian inferential approach yield estimates of the probability distributions for the number, location, and extent of active regions. Both simulated MEG data and data from a visual evoked response experiment are used to demonstrate the capabilities of this approach.