About MEG Processor

MEG Processor is a software package with new techniques for imaging functional brain activity.

MEG Processor is a native Window program (C/C++, not Matlab Toolbox). The software package supports unique new techniques (e.g. Wavelet-based beamforming, accumulated source imaging, frequency-encoded source imaging) for analyzing functional brain activity.  The neurophysiological principle of the new techniques is that MEG/EEG signals are generated by hierarchical groups of cells; low-frequency signals are generated by large groups of cells and high-frequency signals are generated by small groups of cells.  The core mathematical algorithm is to spatiotemporally and spectrally decompose multi-frequency signals in MEG/EEG data to volumetrically reconstruct brain activity (cell assemble imaging, CAI).  This software program also integrates multiple complementary imaging modalities (EEG, MEG, MRI and CT) in a single package and environment. By combining the latest techniques for determining magnetic and electrical activity in the brain with anatomical and functional imaging, the program provides a powerful new method for accurately reconstructing the source of such activity. The program uses the full physical anatomy from MR and CT to provide three-dimensional models of the head and brain, volumetrically delineating the site of activity.  The novel functionalities make it suitable for wider applications.

The software is freely available from the  website.   

This software package is different from many MEG/EEG toolboxes that are typically based on Matlab/IDL.  For example, this software package provides following outstanding features: 

·        Specially designed data analysis modules to utilizing both low- and high-frequency neuromagnetic/electric signals for characterize brain functions in spatial, temporal and frequency domains.

·        Volumetric source imaging with newly developed source localization algorithms (wavelet-beamformer).

·        Dynamic magnetic source imaging (dMSI) for visualizing sources in real-time or accumulated mode.

·        Voxel multi-coding for precise determination of brain activity/activation while significantly minimizing MEG/EEG inverse problems.

·        New signal processing algorithms which are not available in other software packages (e.g. polarity contour maps, accumulated spectrograms, etc.);

·         Intuitive graphic user interface (GUI), which provides real-time “toolbar-tips” for ease of use or better usability without memorizing all the commands.

·         High-performance and handle a huge amounts of data using assembling codes and parallel computing.

·        Optimized functions for analysis of high-frequency MEG/EEG data (e.g. built-in re-sampling function, handle a huge amounts of MEG/EEG data)

·         Quantitative assessment of coherence of brain activity/activation at source levels using volumetric coherence analysis, which analyze every possible voxel-pair of the entire brain.

·         Outstanding 2D and 3D data visualization tools.

*Prof. Jing Xiang is the Director of MEG Research at Cincinnati Children’s Hospital Medical Center. Dr. Xiang received his formal training in neurology and completed his PhD in magnetoencephalography (MEG). His graduate training included neurophysiology, functional brain mapping, signal processing and computer programming. He worked in Japan (RIKEN), Canada (University of Toronto) and USA (University of Cincinnati). He played a key role in building the world's first pediatric MEG lab in The Hospital for Sick Children in Toronto. He published more than 100 papers and received approximately 20 awards.  Dr. Xiang’s research focuses on clinical applications of MEG in neurology and pediatrics, particularly, high-frequency brain signals.   Dr. Xiang developed the world’s first High-Frequency MEG/EEG Database.  His research has been focused on EEG/ECoG for detection of high-frequency signals for more than 30 years. He has been trying to use MEG to detect, localize and quantify high-frequency signals (e.g. high gamma, high-frequency oscillations, ripple, and fast ripple) for 18 years.

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