A software package running under MATLAB and allowing for analysis and visualization of functional brain networks from M/EEG recordings. The main objective of this tool is to cover the complete processing framework from the M/EEG pre-processing to the identification of the functional brain networks. EEGNET includes mainly the calculation of the functional connectivity between scalp M/EEG signals as well between reconstructed brain sources obtained from the solution of the inverse problem. It also includes the characterization of the brain networks by computing the network measures proposed in the field of graph theory. EEGNET provides user-friendly interactive 2D /3D brain networks visualization.
Processes continuous and event-related EEG (electro-encephalography) and MEG (magneto-encephalography). EEGLAB also processes other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. EEGLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and interactively process their high-density EEG and other dynamic brain data using ICA and/or time/frequency analysis, as well as standard averaging methods.
Assists in visualizing results of processing steps and final outputs. MNE-Python is a scripting-based package with many visualization capabilities. It covers multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. It offers some unique capabilities, in a coherent package facilitating the combination of standard and advanced techniques in a single environment.
Interfaces standard electrophysiology data sources and collates various analysis and simulation tools. FIND allows concise review of methods and algorithms, and opens possibilities for enhancements. It offers feature for the unified data import, representation and storage allowing for a standardized interfacing, independently of the experimental hardware and software used. This tool provides effective means of quality management.
Aids users to generate realistic head models from available data (MRI and/or electrode locations). NFT contains tools permitting researchers to work on several topics: segmenting scalp, skull, cerebrospinal fluid (CSF) and brain tissues from T1-weighted magnetic resonance (MR) images. In summary, this program offers two main actions: generation of realistic head models and calculation of numerical solution of the forward problem of electromagnetic source imaging.
Allows to visualize, process, and integrate with anatomical magnetic resonance imaging (MRI) data, magnetoencephalography (MEG) and electroencephalography (EEG) data. Brainstorm aims to provide a set of tools to the scientific community using MEG/EEG as an experimental technique. User can interact with MEG/EEG recordings including various displays of time series, topographical mapping on 2D or 3D surfaces, generation of animations and series of snapshots of identical viewpoints at sequential time points, the selection of channels and time segments, and the manipulation of clusters of sensors.
A preprocessor for the analysis of intra- or extracellular neuronal recordings. NEV2lkit’s main objective is to supply a friendly user interface that links the experimental data to a basic set of routines for analysis, visualization and classification of spikes in a consistent framework. It performs fast unit sorting in single or multiple experiments and allows the extraction of spikes from over large time intervals in continuously recorded data streams.
Offers a method for magnetoencephalography and electroencephalography (M/EEG) patch source imaging on high-resolution cortices. STRAPS is state-space modeling and estimation algorithm that uses local spatial-temporal constraints for estimating cortical sources. The algorithm was tested on both synthetic electroencephalography (EEG) data the numerical simulations and real Magnetoencephalography (MEG) data analysis.
Relates electro- and magnetoencephalography (EEG/MEG) data to a given target variable. SPoC can discover a spatial filter that extracts an oscillatory signal whose power modulation follows a given target variable. It solves the problem of component extraction for band power correlation/covariance. This tool is useful to extract information from auditory sources generating steady-state responses.
A framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). A BIDS App is a container image capturing a neuroimaging pipeline that takes a BIDS-formatted dataset as input. Each BIDS App has the same core set of command line arguments, making them easy to run and integrate into automated platforms. BIDS Apps are constructed in a way that does not depend on any software outside of the container mage other than the container engine.
A statistical framework for neuroimaging data analysis based on mutual information estimated via a Gaussian copula. GCMI estimates mutual information with continuous variables. This method is rank-based, robust and makes no assumptions on the marginal distributions of each variable. It does make an assumption on the form of the relationship between the variables, which results in the estimate being a lower bound to the true MI. It is computationally efficient and statistically powerful when applied within a permutation-based null-hypothesis testing framework.
Provides a platform for analyzing neuronal networks recorded on microelectrode arrays (MEAs). meaRtools is composed of four main modules for: (i) determining simple and complex single-electrode and network (multiple electrodes) activity phenotypes; (ii) merging data from multiple recordings of the same experiment; (iii) performing reproducible case/control based statistical analysis and; (iv) visualizing the results in presentable ready-to-use graphs and charts.
Produces image maps of the sources of neural oscillations from magnetoencephalography/ electroencephalography (MEG/EEG) sensor data. iES performs imaging with embedded inferential and group prevalence statistics altogether. The software identifies source patterns that are challenging to standard approaches, especially when masked by field-spread from other sources in the volume. It can be useful for research using M/EEG imaging, where group-level inferences are very common.
Identifies epileptic seizures. SozRank constructs an empirical distribution of the scores calculated over random blocks recorded while the patient is resting to proceed. It is based on a combination of a parametric causality measure, Granger causality, and a non-parametric measure, directed information. This tool is able to quantify the pair-wise causal influences between the recordings.
Analyzes and visualizes electrophysiological data. Stimfit is a free analysis environment for cellular neurophysiology. This tool can number and clarify the activity of single neurons and communication between neurons. It contains algorithms to characterize the kinetics of presynaptic action potentials. This software uses several typical formats for biomedical signals, including those most commonly used in cellular electrophysiology.
Analyzes, browses and analyzes data from electrophysiology experiments or neural simulations. Spyke Viewer is built on an object model for electrophysiology data: the Neo library. This software furnishes a central graphic user interface from which independently developed analysis can be executed. It provides a flexible plugin architecture that permits creation of new plugins to allow extensibility.
Provides a way to design electrophysiological experiments. PLDAPS consists of a layout for connecting several pieces of hardware, and a set of sample scripts and functions that demonstrate how they can be functionally integrated. It can perform experiments that rely on adapting the stimuli and experimental states contingent. The tool can be useful in the context of single and multi-unit electrophysiology in awake, behaving primates.
Performs electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) multimodal fusion. NIT provides several modules designed for building temporal and/or spatial information for EEG-fMRI multimodal fusion investigation. It offers different hemodynamic response function (HRF) shapes, including single gamma, standard statistical parametric mapping (SPM) and Glover HRFs. This tool aims to facilitate multimodal fusion study.
Assists users in the detection of high-frequency oscillations (HFOs) and in the identification of the seizure-onset zone (SOZ). EPINETLAB is a multi-GUI EEGLAB plugin that can be useful to research teams working on epilepsy presurgical workup data. It allows analysis of multiple files in a single process. This method also implements a robust channel reduction methodology designed to reduce computational load and subject-dependent errors.
Permits to visualize electrodes implantation on image data and prepares database for group studies. IntrAnat Electrodes allows users to search across patients according to a variety of anatomical and functional criteria. It uses established research neuroimaging toolboxes such as BrainVisa, Freesurfer, SPM and ANTs to recycle optimal and validated image analysis processes.