Difference between revisions of "BESA Research Manual"
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|title = Module information | |title = Module information | ||
|module = BESA Research Basic or higher | |module = BESA Research Basic or higher | ||
| − | |version = 6.1 or higher | + | |version = BESA Research 6.1 or higher |
}} | }} | ||
<span style="font-size: 150%;"><strong>Welcome to BESA Research</strong></span> | <span style="font-size: 150%;"><strong>Welcome to BESA Research</strong></span> | ||
| − | BESA Research is the most widely used software for source analysis and dipole localization in EEG, MEG, Evoked Potentials and ERP research. BESA Research has been developed on the basis of over 20 years | + | BESA Research is the most widely used software for source analysis and dipole localization in EEG, MEG, Evoked Potentials and ERP research. BESA Research has been developed on the basis of over 20 years of experience in human brain research by Michael Scherg, University of Heidelberg, and Patrick Berg, University of Konstanz. |
| − | BESA Research provides many advanced features for automatic source localization, fast | + | BESA Research provides many advanced features for automatic source localization, fast modelling and easy, interactive hypothesis testing. Source analysis can be performed simultaneously on multiple conditions with advanced constraints based on anatomy and physiology. In addition to discrete multiple dipole modelling, all major distributed imaging methods are also available for comparison. This makes BESA a complete all-in-one tool for source imaging. |
| − | BESA has all the features required to perform offline processing of continuously acquired EEG and MEG data using external and internal triggers, e.g. generated from an EMG channel or by spatio-temporal pattern search. Triggers are automatically mapped into predefined paradigms to provide for fast selection of combinations of averages (addition, subtraction, subsets). Based on the spatial components approach of Berg and Scherg (1994), artifacts (e.g. eye and ECG) can be corrected on the fly from EEG, MEG, and ERP data. | + | BESA has all the features required to perform offline processing of continuously acquired EEG and MEG data using external and internal triggers, e.g. generated from an EMG channel or by spatio-temporal pattern search. Triggers are automatically mapped into predefined paradigms to provide for a fast selection of combinations of averages (addition, subtraction, subsets). Based on the spatial components approach of Berg and Scherg (1994), artifacts (e.g. eye and ECG) can be corrected on the fly from EEG, MEG, and ERP data. |
Special viewing options allow for easy selection of averaged or single epochs of interest (e.g. spikes) and for immediate source localization and analysis. Digitized 3D sensor locations can be used to coregister EEG or MEG data with structural and functional MRI. Fitted dipole sources can be superimposed directly to the individual MR image. Using the interactive link with the famous BrainVoyager software of Dr. Rainer Goebel, BOLD clusters in functional MRI (fMRI) can be used directly as seeds for dipoles sources. | Special viewing options allow for easy selection of averaged or single epochs of interest (e.g. spikes) and for immediate source localization and analysis. Digitized 3D sensor locations can be used to coregister EEG or MEG data with structural and functional MRI. Fitted dipole sources can be superimposed directly to the individual MR image. Using the interactive link with the famous BrainVoyager software of Dr. Rainer Goebel, BOLD clusters in functional MRI (fMRI) can be used directly as seeds for dipoles sources. | ||
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[[BESA Research Artifact Correction|Artifact Correction]] | [[BESA Research Artifact Correction|Artifact Correction]] | ||
| − | [[BESA Research Spectral Analysis|Spectral Analysis]] | + | [[BESA Research Spectral Analysis|Spectral Analysis (FFT and DSA)]] |
[[BESA Research ERP Processing|ERP Processing]] | [[BESA Research ERP Processing|ERP Processing]] | ||
| − | [[BESA Research ICA|ICA]] | + | [[BESA Research ICA|Independent Component Analysis (ICA)]] |
[[BESA Research Batch Processing|Batch Processing]] | [[BESA Research Batch Processing|Batch Processing]] | ||
| Line 38: | Line 38: | ||
[[Source Analysis Introduction|Starting the Source Analysis Module]] | [[Source Analysis Introduction|Starting the Source Analysis Module]] | ||
| + | |||
| + | [[Discrete Sources]] | ||
[[Source Analysis Functions of the Window|Functions of the Source Analysis Window]] | [[Source Analysis Functions of the Window|Functions of the Source Analysis Window]] | ||
| Line 65: | Line 67: | ||
== Special Topics == | == Special Topics == | ||
| − | [[Electrodes and Surface Locations]] | + | [[Electrodes and Surface Locations|Working with Electrodes and Surface Locations]] |
[[The Initialization File: BESA.ini]] | [[The Initialization File: BESA.ini]] | ||
Latest revision as of 14:32, 5 May 2021
| Module information | |
| Modules | BESA Research Basic or higher |
| Version | BESA Research 6.1 or higher |
Welcome to BESA Research
BESA Research is the most widely used software for source analysis and dipole localization in EEG, MEG, Evoked Potentials and ERP research. BESA Research has been developed on the basis of over 20 years of experience in human brain research by Michael Scherg, University of Heidelberg, and Patrick Berg, University of Konstanz.
BESA Research provides many advanced features for automatic source localization, fast modelling and easy, interactive hypothesis testing. Source analysis can be performed simultaneously on multiple conditions with advanced constraints based on anatomy and physiology. In addition to discrete multiple dipole modelling, all major distributed imaging methods are also available for comparison. This makes BESA a complete all-in-one tool for source imaging.
BESA has all the features required to perform offline processing of continuously acquired EEG and MEG data using external and internal triggers, e.g. generated from an EMG channel or by spatio-temporal pattern search. Triggers are automatically mapped into predefined paradigms to provide for a fast selection of combinations of averages (addition, subtraction, subsets). Based on the spatial components approach of Berg and Scherg (1994), artifacts (e.g. eye and ECG) can be corrected on the fly from EEG, MEG, and ERP data.
Special viewing options allow for easy selection of averaged or single epochs of interest (e.g. spikes) and for immediate source localization and analysis. Digitized 3D sensor locations can be used to coregister EEG or MEG data with structural and functional MRI. Fitted dipole sources can be superimposed directly to the individual MR image. Using the interactive link with the famous BrainVoyager software of Dr. Rainer Goebel, BOLD clusters in functional MRI (fMRI) can be used directly as seeds for dipoles sources.
Information about updates and new releases can be found on the BESA homepage http://www.besa.de
Contents
Review
Spectral Analysis (FFT and DSA)
Independent Component Analysis (ICA)
Source Analysis
Starting the Source Analysis Module
Functions of the Source Analysis Window
Integration with MRI and fMRI
Source Coherence
Source Coherence Introduction and Concepts
Export
MATLAB Interface
Special Topics
Working with Electrodes and Surface Locations
The Initialization File: BESA.ini
| Review | |
|---|---|
| Source Analysis | |
| Integration with MRI and fMRI | |
| Source Coherence | |
| Export | |
| MATLAB Interface | |
| Special Topics | |
