Source localization from multichannel EEG/MEG: mathematics or neuroinformatics?
نویسندگان
چکیده
منابع مشابه
Scalable Source Localization with Multichannel Alpha-Stable Distributions
In this paper, we focus on the problem of sound source localization and we propose a technique that exploits the known and arbitrary geometry of the microphone array. While most probabilistic techniques presented in the past rely on Gaussian models, we go further in this direction and detail a method for source localization that is based on the recently proposed α-stable harmonizable processes....
متن کاملOpen source software development for neuroinformatics
According to the Mission Statement of this journal, neuroinformatics is a field devoted to the development of neuroscience data and knowledge bases together with numerical models and analytical tools for the sharing, integration and analysis of experimental data and the advancement of theories of nervous system function. An important distinguishing feature of neuroscience is the heterogeneity a...
متن کاملLost in localization: A solution with neuroinformatics 2.0?
The commentary by Derrfuss and Mar (Derrfuss, J., Mar, R.A., 2009. Lost in localization: The need for a universal coordinate database. NeuroImage, doi:10.1016/j.neuroimage.2009.01.053.) discusses some of the limitations of the present databases and calls for a universal coordinate database. Here I discuss further issues and propose another angle to the solution of a universal coordinate databas...
متن کاملNoninvasive Localization of Cardiac Arrhythmia Sources from Multichannel Ecg Measurements
Results of noninvasive localization of arrhythmogenic tissue in the heart from multichannel ecg measurements are presented. Location of initial preexcitation was estimated by inverse computations using multiple dipole (MD) or jumping dipole (JD) model of equivalent heart generator. First, the influence of error factors frequently met in practice was analyzed on simulated ecg data. Mean localiza...
متن کاملExtraction of a source from multichannel data using sparse decomposition
It was discovered recently that sparse decomposition by signal dictionaries results in dramatic improvement of the qualities of blind source separation. We exploit sparse decomposition of a source in order to extract it from multidimensional sensor data, in applications where a rough template of the source is known. This leads to a convex optimization problem, which is solved by a Newton-type m...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Frontiers in Neuroinformatics
سال: 2008
ISSN: 1662-5196
DOI: 10.3389/conf.neuro.11.2008.01.015