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 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

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