EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing

By Arnaud Delorme, T. Mullen, Christian Andreas Kothe, Zeynep Akalin Acar, Nima Bigdely Shamlo, Andrey Vankov, Scott Makeig. Computational Intelligence and Neuroscience, 2011

655 citations recorded by the source corpus.

We describe a set of complementary EEG data collection and processing tools recently developed at the Swartz Center for Computational Neuroscience (SCCN) that connect to and extend the EEGLAB software environment, a freely available and readily extensible processing environment running under Matlab. The new tools include (1) a new and flexible EEGLAB STUDY design facility for framing and performing statistical analyses on data from multiple subjects; (2) a neuroelectromagnetic forward head modeling toolbox (NFT) for building realistic electrical head models from available data; (3) a source information flow toolbox (SIFT) for modeling ongoing or event-related effective connectivity between cortical areas; (4) a BCILAB toolbox for building online brain-computer interface (BCI) models from available data, and (5) an experimental real-time interactive control and analysis (ERICA) environment for real-time production and coordination of interactive, multimodal experiments.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/916bcbcfff6f52dc1f632fc8cd980e86d76ab77e

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In Cognitive Neuroscience

Citations in the corpus

EEG Source Imaging: A Practical Review of the Analysis Steps (2019, methodology): “A comprehensive overview of different academic software applications can be found in a special issue of the Journal Computational Intelligence and Neuroscience (30), where programs such as BrainStorm (31), EEGLAB (32), FieldTrip (33), NUTMEG (34), SPM (35), andCartool (29) are described.”
Digital filter design for electrophysiological data--a practical approach. (2015, positive): “The EEGLAB 13 (SVN rev. 10348; Delorme and Makeig, 2004; Delorme et al., 2011) default filter is the “Basic FIR filter (new)” implementing zero-phase Hamming-windowed sinc FIR filter on the basis of the firfilt EEGLAB plugin (version 1.”
HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity (2013, methodology): “For a given connectivity index, the results for the M variables (whether electrodes, MEG sensors or sources) are stored in the interdependence matrix A for each subject.”
Real-time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG (2015, positive): “We have made the workflow available as the BCILAB "Source Information Flow Toolbox Adapter" paradigm (ParadigmSIFT class).”
MEG and EEG data analysis with MNE-Python (2013, methodology): “Several of such software packages for M/EEG data processing exist, including Brainstorm (Tadel et al., 2011), EEGLAB [Delorme and Makeig (2004) and Delorme et al. (2011)], FieldTrip (Oostenveld et al., 2011), NutMeg (Dalal et al., 2011) and SPM (Litvak et al., 2011).”
Real-Time Modeling and 3D Visualization of Source Dynamics and Connectivity Using Wearable EEG (2013, neutral): “1) Simulated Data: To test the ability of our pipeline to accurately reconstruct source dynamics and connectivity in real-time, we generated a five-dimensional VAR[3] system of coupled oscillators as described in Eq.”
ICLabel: An automated electroencephalographic independent component classifier, dataset, and website (2019, positive): “The end result is made freely and easily available through the ICLabel plugin for the EEGLAB software environment (Delorme and Makeig, 2004; Delorme et al., 2011).”
MNE software for processing MEG and EEG data (2013, methodology): “, 2011), EEGLAB (Delorme and Makeig, 2004; Delorme et al., 2011), FieldTrip (Oostenveld et al.”
A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls (2016, methodology): “We note that other toolboxes are also available for estimation of functional connectivity data, most notably EEGLAB and the SIFT toolbox (Delorme et al., 2011), the MVGC toolbox (Barnett and Seth, 2014), the Hermes toolbox (Niso et al., 2013), the TRENTOOL toolbox (Lindner et al., 2011) for…”
Nested oscillations and brain connectivity during sequential stages of feature-based attention (2020, methodology): “EEG preprocessing was performed using a combination of EEGLAB (Delorme et al., 2011; Delorme and Makeig, 2004), its plugins, and in-house scripts implemented in MATLAB (The MathWorks Inc.”