1 - 31 of 31 results

SEEGA / SEEG Assistant

Provides a set of tools integrated in a single 3DSlicer extension, which aims to assist neurosurgeons in the analysis of post-implant structural data and hence aid the neurophysiologist in the interpretation of SEEG data. SEEGA consists of (i) a module to localize the electrode contact positions using imaging data from a thresholded post-implant CT, (ii) a module to determine the most probable cerebral location of the recorded activity, and (iii) a module to compute the Grey Matter Proximity Index, i.e. the distance of each contact from the cerebral cortex, in order to discriminate between white and grey matter location of contacts. Finally, exploiting 3DSlicer capabilities, SEEGA offers a Graphical User Interface (GUI) that simplifies the interaction between the user and the tools.

GCMI / Gaussian-Copula Mutual Information

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.

BIDS Apps / Brain Imaging Data Structure Applications

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.


Proposes a method for rebuild task-related sources. The application proposes electroencephalography (EEG) source imaging model based on temporal graph regularized low-rank representation composed of: (i) data fitting term, (ii) temporal graph embedding regularization term, and (iii); a ℓ1 norm for sparsity penalty and nuclear norm. This model is solved by an algorithm using the alternating direction method of multipliers (ADMM) that is able to extract low-rank task-related source patterns.

ECoG ClusterFlow

Allows neuroscientists to investigate the major cluster evolution patterns over space and time. ECoG ClusterFlow is a hierarchical multi-scale approach that supports the exploration, comparison and analysis of time-varying community evolution patterns at varying temporal granularity. It provides (i) an overview that summarizes the overall changes in cluster evolution, where users explore salient dynamic patterns; and (ii) a hierarchical glyph-based timeline visualization for exploring the dynamic spatial organizational changes of the clusters that uses data aggregation and small multiples methods.

iQSA / improved Quaternion-based Signal Analysis

Provides an improvement to the quaternion-based signal analysis (QSA) method. iQSA is an analysis algorithm for use in the feature extraction and classification phases. It consists in providing a technique for use in real-time applications, focusing on analyzing extract electroencephalography (EEG) signals online reducing the sample sizes needed to a tenth of the ones required by QSA. It results in a faster response and fewer delays to improve execution times in real-time actions.


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.


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.

eConnectome / Electrophysiological Connectome

A MATLAB-based toolbox, eConnectome (electrophysiological connectome), for mapping and imaging functional connectivity at both the scalp and cortical levels from the electroencephalogram (EEG), as well as from the electrocorticogram (ECoG). Graphical user interfaces were designed for interactive and intuitive use of the toolbox. Major functions of eConnectome include EEG/ECoG preprocessing, scalp spatial mapping, cortical source estimation, connectivity analysis, and visualization. Granger causality measures such as directed transfer function and adaptive directed transfer function were implemented to estimate the directional interactions of brain functional networks, over the scalp and cortical sensor spaces. Cortical current density inverse imaging was implemented using a generic realistic geometry brain-head model from scalp EEGs. Granger causality could be further estimated over the cortical source domain from the inversely reconstructed cortical source signals as derived from the scalp EEG.

STRAPS / Spatio-Temporally Regularized Algorithm for m/eeg Patch Source imaging

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.

s-SMOOTH / Sparsity and SMOOthness enhanced brain TomograpHy

Proposes a method for improving reconstruction accuracy for electroencephalography (EEG) source imaging. s-SMOOTH is an algorithm that merges (i) voxel-based Total Generalized Variation (vTGV) to promote sparsity on the spatial derivative and (ii) the ℓ1−2 regularizations to impose sparsity on the current density itself. This approach aims to estimate the location, extent and magnitude variation of the current density distribution.


A Matlab-based software for the visualization of multi-channel biomedical signals, particularly for the electroencephalography (EEG). BioSigPlot is designed for researchers on both engineering and medicine who should collaborate, visualize and analyze signals. It aims to provide a highly customizable interface for signal processing experimentation in order to plot several kinds of signals while integrating the common tools for physician. The main advantages compared to other existing programs are the multi-dataset displaying, the synchronization with video and the online processing.


Basic wavelet analysis of multivariate time series with a visualisation and parametrization using graph theory. Brainwaiver computes the correlation matrix for each scale of a wavelet decomposition, namely the one performed by the R package waveslim (Whitcher, 2000). An hypothesis test is applied to each entry of one matrix in order to construct an adjacency matrix of a graph. The graph obtained is finally analysed using the small-world theory (Watts and Strogatz, 1998) and using the computation of efficiency (Latora, 2001), tested using simulated attacks. The brainwaver project is complementary to the camba project for brain-data preprocessing.


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.

GAT / Graph-Analysis Toolbox

Facilitates analysis and comparison of structural and functional network brain networks. GAT provides a graphical user interface (GUI) that facilitates construction and analysis of brain networks, comparison of regional and global topological properties between networks, analysis of network hub and modules, and analysis of resilience of the networks to random failure and targeted attacks. Area under a curve (AUC) and functional data analyses (FDA), in conjunction with permutation testing, is employed for testing the differences in network topologies; analyses that are less sensitive to the thresholding process.


Infers functional connectivity by analyzing the peak trains of spiking neuronal signals. ToolConnect implements correlation and information theory based core algorithms. It contains several modules in the windows form embodiment and allows the user to easily manipulate and analyze data, while providing computational efficiency and accuracy. This tool is able to return both numerical and graphical results. It has been designed to be adapted, modified and extended by the interested researchers.