EEG Data Analysis BESA Research - EEG Evolution from BESA 6 to 7
EEG Data Analysis BESA Research - EEG Evolution from BESA 6.0 to 7.1
Evolution from version 6.0 to 7.1
BESA Connectivity:
• Stand-alone module with 64-bit architecture and modern workflow design, seamless
integration with BESA Research
• Use wavelets and / or complex demodulation
• Analyze connectivity in source space or sensor space
• Latest connectivity methods including Granger Causality, PDC, ...
• Visualize data in clear 2D and 3D result plots and create images or videos
• Export results for further analysis in e.g. BESA Statistics
BESA Research
Main new features evolving from 6.0 to 7.1
Review | Source |Analysis | Source
Coherence /Connectivity
• Age-appropriate template models
• Cortical Loreta and Cortical CLARA
• Visualization on inflated cortex
• Resting-state network source montages
• Simultaneous EEG-fMRI processing
• New ICA method: SOBI
• New readers: Neuroscan CURRY 7, Neuralynx, RICOH
• Bayesian source imaging with SESAME – fully automated dipolar source localization
• Time-domain beamforming using several methods
• Brain atlases
• Atlas-based source montages
• Parallel computing
• New readers: XDF, Neuroscan CURRY 8
• Many new batch commands
• Boundary element model integration
• Combined MEG-EEG source modelling
• Confidence limit calculation
• Beamformer virtual sensor montages based on atlas regions
• Multi-slice view
• Full noise covariance matrix in MinNorm
BESA MRI 2.0
Cortex inflation
BESA Research 6.1
Age-appropriate template models
Cortical LORETA and Cortical CLARA
Resting state networks
BESA Research 6.1
Age-appropriate template models
• Realistic EEG head models for children in 2-year stages of development
• Non-linear co-registration of brains preserved internal structures
• kindly provided by John Richards
• Inflation of 12y+ age models available
• 20yr-model could substitute the current MRI standard brain in future
BESA Research 6.1
Cortical LORETA and Cortical CLARA
• Surface Laplacian implemented in three different versions (based on Master thesis at Univ Sofia)
• Graph Laplacian
• Geometric Laplacian without area weights
• Geometric Laplacian with area weights
• Uses only cortical locations as source space
• Improvement over other implementations which do not work on the surface
• Works with individual meshes
• Publication in preparation on Cortical LORETA (Jordanov et al.)
BESA Research 6.1
Cortical LORETA and Cortical CLARA
• Cortical CLARA: Auditory stimulus
(EEG, N100)
• Visual motion MEG, 160ms
• Visual motion MEG, 122ms
BESA Research 6.1
Resting state networks
Fronto-parietal task control network
DMN Dorsal attention Fronto-par. Task con. Ventral attention
Resting state networks
Example: Frontal-parietal task control network montage, applied to ERN experiment
• Task: Press a button if a certain (visual) condition is fulfilled
• About 25% erroneous answers
Resting state networks
Example: Frontal-parietal task control network montage, applied to ERN experiment
• Observe pre-stimulus epoch for network activity
• Activity is seen in channel RdlPFC at approx. 24Hz around stimulus time
• Strongest for correct responses
A whole new set of features enables cutting-edge processing pipelines for your M/EEG research tasks
EEG-fMRI processing Connectivity analysis Beamforming
EEG Data Analysis EEG-fMRI Cortex inflation LORETA CLARA
EEG Data Analysis EEG-fMRI Cortex inflation LORETA CLARA
EEG Data Analysis Source analysis noise covariance
EEG Data Analysis Source analysis noise covariance
EEG Data Analysis beamformer virtual sensor montages based on atlas regions
EEG Data Analysis beamformer virtual sensor montages based on atlas regions
EEG Data Analysis Boundary element model BEM
EEG Data Analysis Boundary element model BEM
EEG Data Analysis Bayesian source imaging
EEG Data Analysis Bayesian source imaging
EEG Data Analysis EEG-fMRI imaging
EEG Data Analysis EEG-fMRI imaging
EEG Data Analysis Brain atlases
EEG Data Analysis Brain atlases
EEG Data Analysis Beamforming
EEG Data Analysis Beamforming
EEG Data Analysis Connectivity analysis
EEG Data Analysis Connectivity analysis
EEG Data Analysis BESA Research 70
EEG Data Analysis BESA Research 70
EEG Data Analysis Observe pre-stimulus epoch for network activity
EEG Data Analysis Observe pre-stimulus epoch for network activity
EEG Data Aanlysis Cortical CLARA Auditory stimulus EEG N100
EEG Data Aanlysis Cortical CLARA Auditory stimulus EEG N100
EEG Data Analysis Cortical LORETA and Cortical CLARA
EEG Data Analysis Cortical LORETA and Cortical CLARA
EEG Data Analysis Realistic EEG head models for children in 2-year stages of development
EEG Data Analysis Realistic EEG head models for children in 2-year stages of development
Neuroscience to improve Latin American Identity. Scientific questions and experimental designs for the development of culture, behavior, perception and Latin American consciousness.
Connectivity analysis
• Stand-alone module with 64-bit architecture and modern workflow design
• Use wavelets and / or complex demodulation
• Analyze connectivity in source space or sensor space
• Latest connectivity methods including Granger
Causality, Partial Directed Coherence, ...
• Visualize data in clear 2D and 3D result plots and create publication images or videos
• Seamless integration with BESA Research
• Export results for further analysis in e.g. BESA Statistics
Beamforming
• Time-domain beamformer using one of several state-of-the-art methods
• Virtual sensor mode to reconstruct time courses
• Source montages using the beamformer spatial filter can be applied to raw data
• Compute beamformer image for any time point in interval of interest
• Conveniently select beamformer intervals graphically in ERP module
Brain atlases
• Check the anatomical or functional brain regions of your source imaging or dipole fitting results
• Seed sources from known locations in the brain atlas
• Choose between several state-of-the-art atlases including AAL, Brainnetome, Brodmann, ...
• Visualize source imaging results together with atlas images
• Innovative contour display mode to facilitate brain image review
EEG-fMRI imaging
• Correct your fMRI artifacts directly in the BESA
Research review window
• Choose between three proven methods for
correction with few mouse clicks
• Read your fMRI data directly into BESA Research
• Seed sources from fMRI and directly see activation patterns on millisecond scale
BESA Research 7.0
Bayesian source imaging
• Sequential Semi-Analytic Monte-Carlo Estimation (SESAME) of sources
• Automatically find most likely number of sources
• Compute map of likelihood for source positions
• Choose between most likely solutions for different numbers of sources
• Virtually no user input required
• Uses Markov-Chain Monte-Carlo method for efficient computation of probability distribution
BESA Research 7.1
Data review and pre-processing
New features:
• Atlas-based source montages: Pre-computed atlas-based source montages are now
available from the menu entry Montage/Source/Atlas montages as well as under the Src
button in the control ribbon.
• Parallel computing is used for speed-up of many time-consuming tasks.
• Smoother and faster plotting of waveforms eases review of high-density M/EEG data.
• New data readers for XDF and Neuroscan CURRY 8 formats are available.
Data review and pre-processing
New features:
• Atlas-based source montages: Pre-computed atlas-based source montages are now
available from the menu entry Montage/Source/Atlas montages as well as under the Src
button in the control ribbon.
Source analysis
New features:
• Boundary element model (BEM) integration: Boundary element head models for individual subjects
computed in BESA MRI version 3.0 or higher are automatically loaded in the Source Analysis module,
for EEG and / or MEG.
• Combined MEG-EEG source modelling: Combining MEG and EEG for source imaging is now
possible. Discrete source fitting as well as all distributed source imaging methods can use the combined
model.
• MRI display in multi-slice view: The subject’s MRI with overlay of source images, dipole solutions, atlases, can now be shown in multi-slice view. Navigate through the MRI using the mouse wheel.
• Use of noise covariance data from individual trials: The full noise covariance matrix from individual trials can now be used in computation of minimum norm estimates.
• The Bayesian source imaging method SESAME was improved to enhance robustness, as well as speed of computation and convergence.
• Confidence limit calculation and display: For dipole solutions and oriented regional sources,
confidence limits are now calculated, displayed, and stored.
• Calculation of beamformer virtual sensor montages based on atlas regions is now supported.
• Two new brain atlases were added: Yeo7 and Yeo17.
• Montreal Neurological Institute (MNI) coordinates can now be used in the Source Analysis window.
• The baseline interval definition now features an automatic alert if it interferes with signal of interest.
• Ready-made color schemes for publication purposes are now available.
Source analysis
New features:
• Boundary element model (BEM) integration: Boundary element head models for individual subjects computed in BESA MRI version 3.0 or higher are automatically loaded in the Source Analysis module,
for EEG and / or MEG.
BESA Research 7.1
Source analysis
New features:
• Combined MEG-EEG source modelling: Combining MEG and EEG for source imaging is now
possible. Discrete source fitting as well as all distributed source imaging methods can use the combined model.
Head models for EEG and MEG Noisenormalized channels
Source analysis
New features:
• MRI display in multi-slice view: The subject’s MRI with overlay of source images, dipole solutions, atlases, can now be shown in multi-slice view. Navigate through the MRI using the mouse wheel.
Source analysis
New features:
• Use of noise covariance data from individual trials: The full noise covariance matrix from individual trials can now be used in computation of minimum norm estimates.
A) Run beamformer
B) In Image Settings, check box
C) Run Min Norm
Source analysis
New features:
• The Bayesian source imaging method SESAME was improved to enhance robustness, as well as speed of computation and convergence.
New features:
• Confidence limit calculation and display: For dipole solutions and regional sources, confidence limits are now calculated, displayed, and stored.
Source analysis
New features:
• Calculation of beamformer virtual sensor montages based on atlas regions is now supported.
