Journal: Frontiers in Neuroinformatics

Article Title: EPINETLAB: A Software for Seizure-Onset Zone Identification From Intracranial EEG Signal in Epilepsy

doi: 10.3389/fninf.2018.00045

Figure Lengend Snippet: Functional blocks in EPINETLAB. Four different blocks of functions are defined: (1) Time-frequency transform and statistical analysis. (2) Automated HFO detection and artifact identification. (3) Performance evaluation. (4) Supplementary functions.

Article Snippet: EPINETLAB was implemented as a multi-GUI set of functions to allow users not experienced in the Matlab environment to apply advanced signal processing techniques to datasets acquired during pre-surgical evaluation.

Techniques: Functional Assay

Journal: Frontiers in Neuroinformatics

Article Title: EPINETLAB: A Software for Seizure-Onset Zone Identification From Intracranial EEG Signal in Epilepsy

doi: 10.3389/fninf.2018.00045

Figure Lengend Snippet: EEGLAB main bar with the EPINETLAB plugin installed.

Article Snippet: EPINETLAB was implemented as a multi-GUI set of functions to allow users not experienced in the Matlab environment to apply advanced signal processing techniques to datasets acquired during pre-surgical evaluation.

Techniques:

Journal: Journal of Biomedical Optics

Article Title: Open source software for electric field Monte Carlo simulation of coherent backscattering in biological media containing birefringence

doi: 10.1117/1.JBO.17.11.115001

Figure Lengend Snippet: MATLAB GUI for performing Monte Carlo simulations of CBS. (a) Specification of the general Monte Carlo parameters. (b) Specification of scattering model to be implemented. (c) Specification of the birefringence properties of the ...

Article Snippet: User interaction with the simulation is carried out through the MATLAB GUI shown in Fig. . After specifying the desired parameters, a simulation can be imported into a C code environment for rapid calculation of functions p ( x s ,\u00a0 y s ) and p c ( x s ,\u00a0 y s ) on multiple processors with a single button click.

Techniques:

Journal: Frontiers in Neuroinformatics

Article Title: Robin’s Viewer: Using deep-learning predictions to assist EEG annotation

doi: 10.3389/fninf.2022.1025847

Figure Lengend Snippet: The “Preprocessing” pop-up window is the initial screen of Robin’s Viewer (RV) and has five distinct sections: First, the electroencephalography (EEG)-file selection (see Section “3.1 Loading EEG data”), where the user can load their EEG recording and display selected statistics about the data once it is loaded. Second, the preprocessing settings (see Section “3.2 Preprocessing and visualization settings”), which are used to set a bandpass filter [finite impulse response (FIR) filter with Blackman window] and a custom reference. Third, the bad-channel handling (see Sections “3.2 Preprocessing and visualization settings” and “3.4 Bad-channel marking”), where the user can decide whether to use automatic bad channel detection, and whether to interpolate bad channels. Fourth, the visualization settings (see Section “3.2 Preprocessing and visualization settings”), comprised of downsampling, custom scaling (by default 1e-6 as RV scales data from volts to microvolts for plotting), the gap between traces (by default 40 (μV); setting this to 0 results in butterfly mode where all traces are collapsed on top of each other; values higher than 40 move traces further apart), segment length to plot [by default 60 (seconds)], whether or not to activate the view-slider, and selection of channels to plot. Visualization settings will only be applied to the data for plotting and hence will not be saved in the save-file (in contrast to the preprocessing settings). Fifth, the deep-learning model settings (see Sections “3.2 Preprocessing and visualization settings” and 3.5 Deep-learning model predictions”), where previously saved model output can be loaded and the integrated deep-learning model can be activated to generate predictions. Clicking the “Plot” button at the bottom will close this window and, after a loading screen (which lasts as long as it takes to plot the data), it will open up the main graphical user interface (GUI).

Article Snippet: The main working points are the option to add labels to annotations to support multi-class deep-learning predictions and annotations (e.g., for sleep staging), the possibility to save annotated EEG data in a MATLAB-readable format to better facilitate the integration of RV into MATLAB-based EEG toolboxes, as well as functionality to interact with the RV-GUI using keyboard keys and combinations of keys.

Techniques: Selection

Journal: Journal of nuclear medicine : official publication, Society of Nuclear Medicine

Article Title: Integrated Software Environment Based on COMKAT for Analyzing Tracer Pharmacokinetics with Molecular Imaging

doi: 10.2967/jnumed.109.064824

Figure Lengend Snippet: Summary and Comparison of Functionalities of COMKAT Distributions

Article Snippet: Behind scenes, COMKAT GUI calls COMKAT command-line functions to calculate model output and estimate parameters. table ft1 table-wrap mode="anchored" t5 TABLE 1 caption a7 Function COMKAT on MATLAB Compiled COMKAT application COMKAT GUI Loading of input functions from files Yes Yes Simulation of model output Yes Yes Creation of new kinetic models Yes No Parameter estimation Yes Yes Loading of tissue time–activity curves from files Yes Yes Loading of tissue time–activity curves from COMKAT image tool Yes Yes Calculation of parametric images Yes Yes Distributed computing for parametric imaging * Yes No COMKAT image tool Support for multiple image formatsYes Yes Image display and contrast adjustments Yes Yes Frame summation Yes Yes Spatial filtering Yes Yes Drawing of ROIs or volumes of interest Yes Yes Image coregistration Yes Yes Image translation and rotation Yes Yes Image reslicing in arbitrary orientations Yes Yes MATLAB scripting with COMKAT command-line functions Yes No Available for Windows, Linux, and MacOS X † Yes Yes COMKAT licensing Free for academic research use Free for academic research use MATLAB licensing Requires MATLAB installation and licenses Requires MATLAB Compiler Runtime (no licensing fees) Open in a separate window * Requires MATLAB licenses for MATLAB Distributed Computing Server and Parallel Computing Toolbox.

Techniques: Imaging

Journal: Journal of nuclear medicine : official publication, Society of Nuclear Medicine

Article Title: Integrated Software Environment Based on COMKAT for Analyzing Tracer Pharmacokinetics with Molecular Imaging

doi: 10.2967/jnumed.109.064824

Figure Lengend Snippet: Summary of Computation Speed for Major Functions in COMKAT

Article Snippet: Behind scenes, COMKAT GUI calls COMKAT command-line functions to calculate model output and estimate parameters. table ft1 table-wrap mode="anchored" t5 TABLE 1 caption a7 Function COMKAT on MATLAB Compiled COMKAT application COMKAT GUI Loading of input functions from files Yes Yes Simulation of model output Yes Yes Creation of new kinetic models Yes No Parameter estimation Yes Yes Loading of tissue time–activity curves from files Yes Yes Loading of tissue time–activity curves from COMKAT image tool Yes Yes Calculation of parametric images Yes Yes Distributed computing for parametric imaging * Yes No COMKAT image tool Support for multiple image formatsYes Yes Image display and contrast adjustments Yes Yes Frame summation Yes Yes Spatial filtering Yes Yes Drawing of ROIs or volumes of interest Yes Yes Image coregistration Yes Yes Image translation and rotation Yes Yes Image reslicing in arbitrary orientations Yes Yes MATLAB scripting with COMKAT command-line functions Yes No Available for Windows, Linux, and MacOS X † Yes Yes COMKAT licensing Free for academic research use Free for academic research use MATLAB licensing Requires MATLAB installation and licenses Requires MATLAB Compiler Runtime (no licensing fees) Open in a separate window * Requires MATLAB licenses for MATLAB Distributed Computing Server and Parallel Computing Toolbox.

Techniques: