SfN 2023 MEG EEG Source Analysis BESA

SfN 2023 MEG EEG ERP Source Analysis BESA

Many options for review and pre-processing
Several artifact correction algorithms
Dedicated ERP module
Visualization of results
Handling of paradigms, events, and conditions
Batch scripts for automated processing
ERP displays and tools

Sfn 2023 MEG EEG ERP Source analysis features

Large amount of source reconstruction methods:
discrete
distributed
beamformer
surface-based
Bayesian
time-domain and time-frequency domain

Sfn 2023 MEG EEG ERP BESA EEG and MEG formats

MEG and EEG can now be combined into a single data set for simultaneous source modeling of the two modalities. This is available for many source analysis and source imaging methods.

Sfn 2023 MEG EEG ERP BESA Connectivity

Time-Frequency Analysis
Complex Demodulation
Wavelet Analysis (Morlet or Mexican Hat)
Multitaper
Batch processing of all subject data
Grand Average and individual average review
Connectivity Analysis
Averaging over time and / or frequency ranges matrix
Connectome view using combined time-frequency averages
Circular Connectome view for one-glance overview of connectivity
Directly compare conditions, and connectivity methods
ASCII data result export and input support for MATLAB

Sfn 2023 MEG EEG ERP BESA Interoperability with other software

Matlab interface to directly send data to Matlab
Export of data to ASCII and EDF format
All export formats open and described
Matlab scripts are provided to exchange data between Matlab and BESA Research

Sfn 2023 MEG EEG-fMRI Simultaneous

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

Sfn 2023 MEG EEG Cortical LORETA and CLARA

Use the individual cortex as source space for LORETA and CLARA analysis. BESA Research truly computes the solution on the individual cortex, which is superior to other approaches which simply project the source solution onto the cortex.

SfN 2023 EEG FFT BESA

Spectral analysis of a segment of EEG can be understood as taking a sinusoidal wave (over all cycles contained in the marked block) and shift the wave along the recorded (and filtered) signal. At every latency (or phase shift) we multiply both wave and signal and obtain an output signal of a certain magnitude.

Sfn 2023 EEG BESA Statistics

The main idea behind the cluster-permutation test as it is implemented in BESA Statistics is that if a statistical effect is found over an extended time period in several neighboring channels, it is unlikely that this effect occurred by chance.

Sfn 2023 EEG individual head FEM generation

In an optional step the individual (FEM) head model can be generated based on the segmentation results (see Wolters et al. 2007) and surface meshes of the 4 tissue layers (skin, scalp, CSF and brain) are extracted. The individual FEM head models can be used for EEG or MEG source analysis.

Sfn 2023 EEG BESA Simulator free software

Simulate both EEG and MEG data
See maps resulting from a dipole anywhere within the head
Add any number of dipoles to a model
Generate independent waveforms for each dipole (source waveforms)
See the surface data resulting from the model as reference-free, average reference, Laplacian (CSD) reference, or using any electrode / sensor as reference
Place a cursor anywhere in the time interval and visualize the scalp activity at that time point
Specify the parameters of the spherical head model

Sfn 2023 EEG  Bayesian Source Imaging

SESAME
Cortical LORETA
Cortical CLARA
Minimum Norm
Age-appropriate template head models
Volume imaging using Bayesian methods

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