Swartz Center for Computational Neuroscience

eeglab

EEGLAB is an open source signal processing environment for electrophysiological signals running on Matlab and developed at the SCCN/UCSD

labstreaminglayer

LabStreamingLayer super repository comprising submodules for LSL and associated apps.

ICLabel

Automatic EEG IC classification plugin for EEGLAB

roiconnect

ROI connectivity analysis in EEG

PACTools

EEGLAB plug-in to compute Phase-Amplitude Coupling using different methods

SIFT

SIFT is an EEGLAB-compatible toolbox for analysis and visualization of multivariate causality and information flow between sources of electrophysiological (EEG/ECoG/MEG) activity. It consists of a suite of command-line functions with an integrated Graphical User Interface for easy access to multiple features. There are currently six modules: data preprocessing, model fitting and connectivity estimation, statistical analysis, visualization, group analysis, and neuronal data simulation.

mobilab

MoBILAB toolbox for MATLAB is an analysis and visualization platform for Mobile Brain/Body data.

amica

Code for AMICA: Adaptive Mixture ICA with shared components

bva-io

Routines for loading and saving data files in Brain Vision Data Exchange format (export and import of Brain Vision Analyzer MATLAB files developed by Arnaud Delorme)

EEG-BIDS

cleanline

Clean Line

groupSIFT

neuroscanio

Function to import Neuroscan data into EEGLAB

fMRIb

fMRI artifact correction

PowPowCAT

NFT

Neuroelectromagnetic Forward Modeling Toolbox

get_chanlocs

Electrode localization using 3D head image. EEGLAB plug-in using Fieldtrip toolbox

NEMAR-pipeline

imat

Independent Modulators Analysis Toolbox

sccn.github.io

Host web content of EEGLAB wiki

trimOutlier

viewprops

EEGLAB plugin with improved pop_prop and more

PACT

eegstats

ARfitStudio

eeglab_tests

ctfimport

HEDTools

HED plug-in for EEGLAB

std_dipoleDensity

zapline-plus

Improvements of the ZapLine function to remove line noise from EEG/MEG data. Adds automatic detection of the number of components to remove, and chunks the data into segments to account for nonstationarities.