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.
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.
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.
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.
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.
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.
An open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. FieldTrip is implemented as a MATLAB toolbox and includes a complete set of consistent and userfriendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level.
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.
Provides a data acquisition (DAQ) system. MANTA can acquire data from both analog and digital headstages. It facilitates and encourages the addition of new hardware and analysis tools. The tool supports natural visualization of arbitrary 1D, 2D and 3D electrode array geometries, integrates filtering, spike-sorting and denoising for online display, synchronization and communication with a control unit. It allows two modes of acquisition: locally controlled and remotely triggered.
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.
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.
Assists users in providing heart rate variability analysis. RHRV provides utility functions for importing heart rate time series data, analyzing their variability, and plotting and/or exporting the analysis results. It uses a custom data structure to store all information related to digital recordings from a polysomnograph. It also includes functions for loading ASCII or WFDB formatted files.
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.
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.
Exploits zebrafish larval cardiac videos to compute heart rates. This application proposes a method based on brightness intensity of each video frame and can be applied without a manual pre-selection of a region of heart. This software provides filters to identify the heart region automatically or to withstand background fluctuations during the video recording stage. It can also be extended to compute other cardiac functions such as heartbeat regularity.
Provides a toolbox for electrophysiological data analysis. NeoAnalysis is based on the Neo package to standardize electrophysiological data. This software is composed of six main modules that: (1) converts recording files different data acquiring systems to standardized format (HDF5); (2) detects spikes the raw signals; (3) sorts offline spike; (4) filters analog signals; (5) analyzes data at the population level and (6) displays data and analysis results.
Aims to provide a solution to the development of classifiers for emotion recognition based on electroencephalography. EEG-Black-Hole builds an initial multiclass support vector machine (SVM) classifier by preprocessing the electroencephalogram (EEG) signal. It is useful in emotion sorting if the research aims to obtain relevant information in real time, for instance, incorporating an EEG in the classroom.
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.
Serves for modeling ongoing or event-related effective connectivity between cortical areas. SIFT consists of a toolbox that can be used for detecting and displaying multivariate causality and information flow between sources of electrophysiological (electroencephalography (EEG)/ ElectroCorticoGraphy (ECoG/MEG) activity. This tool is composed of six modules: neuronal data simulation, group analysis, visualization, statistical analysis, model fitting and connectivity estimation, and data preprocessing.
Assists for the analysis of beat-to-beat variability in heart rate. This application is a software for Heart Rate Variability that needs Matlab data in the following format: three variables, each one a single column and all of the same length, that contain: (i) the data values recorded, (ii) time stamps giving the time of occurrence in seconds of each data point in the first variable and (iii) numerical labels marking each datum.
Implements an algorithm to automatically detect the main LFPs features. LFP analysis is a MATLAB software, exploiting Phillips-Tikhonov regularization to automatically detect the first maximum, the following negative peak, and the first time-derivative value in the inflection point between them, in LFPs evoked by whisker stimulation in rat barrel cortex. the algorithm may be extended to estimate other features of interest, such as the duration of the positive deflection and of the negative valley.
Features electrode-level and network activity on a Multi-Electrode Arrays (MEA) plate. 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.
Performs multivariate pattern analysis (MVPA) on the high-temporal-resolution electroencephalography (EEG) data, using univariate analyses of event-related potentials (ERPs). DDTBOX is able to deal with data preprocessed with many other commercially available software packages. For performing, this tool requires users to define discrimination groups, corresponding to categories of interest. Moreover, it can conduct generalization analysis, testing whether patterns of information that discriminate between categories are stable across experimental contexts.
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.
Allows users to manage the inter-individual differences in Electrocardiogram (ECG) beat morphologies for abnormal beat detection. QPDDD consists in a one class classifiers (OCCs) and requires information about beats during its training to work. It operates over non-metric dissimilarities and permits to study a larger set of problem domains.
Furnishes a method for tracking real-time auditory attention from non-invasive M/EEG recordings. Real-time-Tracking-of-Selective-Auditory-Attention is a software, based on Bayesian filtering, that performs in three steps: (i) estimation of dynamic models of encoding and decoding in real-time; (ii) extracting an attention-modulated feature; and (iii) determination of the given feature by using a state-space simulator and translation of the results to provide an evaluation of the attentional state.
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.
Allows users to view 2D slices and renderings of brain imaging data. MRIcroGL is a medical image viewer that permits users to draw regions of interest which can aid lesion mapping and functional magnetic resonance imaging (fMRI) analysis. This method permits users to load overlays such as statistical maps. It can also be used with scripts to show brain in different ways such as glass projection, a clip plane, a shell effect, merged slabs, or an exploded effect.
Optimizes functional magnetic resonance imaging (fMRI) analysis of data derived from high motion pediatric and clinical subjects. ArtRepair is a module, compatible with SPM2, 5, 8 and 12, intending to identify and correct artifacts at three different levels (voxel, volume and slice). This application comprises five mains functions that includes noise filtering, an utility to repair outlier volumes as well as a feature to display the quality of estimates produced by SPM.
Allows users to process intracranial electroencephalography (EEG) recordings. ImaGIN is a module, that can be run as part of the SPM software, enabling the performing of data related to epilepsy or contact charge electrophoresis (CCEP). The application is able to support a wide range of formats including Deltamed Coherence-Neurofile export as well as to handle stereoelectroencephalography (sEEG) and electrocorticography (ECoG) information.
Simplifies clamp control and data acquisition. Henry’s EP Suite includes features for image and spectrographic acquisition, and provides access for protocol editing, inbuilt audio monitor, real-time graphical chart recorder and clamp monitors, real-time steady-state current-voltage monitoring, and sketchpad for runtime comparison.
Assists users to perform interactive control, experimental record and analysis of electroencephalography (EEG). ERICA is a solution allowing researchers to work on multimodal experiments incorporating feedback control and interactive data-adaptive stimulus: neurofeedback, videogame-like protocols, social neuroscience experiments, or cognitive monitoring. Moreover, it is composed of five complementary software environments: (1) a MatSound MaxMSP patch toolbox; (2) an Enactor Matlab environment; (3) a MatRiver LSL client environment; (4) a SNAP environement; and (5) a Lab Streaming Layer (LSL).
Investigates shared information across modalities. FIT proposes three different methods: (i) the joint independent component analysis (ICA), that extracts maximally spatially independent maps for each task that are coupled together by a shared loading parameter; (ii) the CCA + joint ICA, that extracts both shared and distinct sources across features and mixing coefficients and (iii) the parallel ICA which extends ICA for analyzing multiple modalities.
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.
Permits to store and display timeseries files. EDFbrowser supports Electroencephalogram (EEG), Electromyogram (EMG), Electrocardiogram (ECG), or BioImpedance files such as other type of timeseries files. It allows Z-EEG measurement and precise measurements by using crosshairs. The tool provides an annotation editor, a header editor and zoom functions. It permits to fix the EDF-header in case where the value for digital maximum is lower than digital minimum.
Allows detection of changes in brain activity from neuroimaging data. The SPM purpose is to analyze brain imaging data sequences. The analyzed sequences can be time-series from the same subject or a series of images from different cohorts.
Provides a method for Heart Rate Variability (HRV) analysis. HRV toolkit is a rigorously validated package of open source including visualization of normal sinus (NN) interval time series, automated outlier removal, and calculation of the basic time- and frequency-domain HRV statistics widely used in the literature.