Resources for Broadening the Time-Course of EEG and ERP Components
EEGLAB (Delorme A, Makeig S., 2004): Since its inception, EEGLAB used through MatLab is the leading open-source software for all types of EEG analysis, from ERPs, time-frequency components, and more.
1. EEGLAB Mailing list: Great resource for questions and answers.
2. EEGLAB Wiki: Detailed overview and tutorials on using EEGLAB.
3. Full 200+ page tutorial resource: Comprehensive documentation about how to use EEGLAB.
Many individual contributors have created a number of effective and accessible plugins for EEGLAB, some of which are referenced here. From automatic labeling and classification of ICA components to source-localization techniques, there is something for everyone. For a full list, see the EEGLAB plugin page.
Event-Related Potentials (ERPs):
ERPLAB (Lopez-Calderon, J., & Luck, S. J., 2014): An easy-to-use plugin for EEGLAB to make ERP processing, visualization, and analysis simple and easy for everyone. Includes extensive documentation and is accessible to those of all programming skill levels.
Steve Luck’s book, “An Introduction to the Event-Related Potential Technique”, is a great introduction for beginners and comprehensive resource for experts (Luck, S. J., 2014).
SCCN Time-Frequency Tutorials (EEGLAB): Using EEGLAB to conduct time-frequency analysis.
Mike X. Cohen’s book, “Analyzing Neural Time Series Data: Theory and Practice”, is an excellent accessible text that simplifies time-frequency analysis for those without heavy mathematical background and is also a great resource for all from beginners to experts (Cohen, M. X., 2014).
PCA Toolkit: Easy-to-use, and well-documented, MatLab toolbox for Principal Component Analysis (PCA) of both ERPs and Time-Frequency components (Dien, J., 2010).
BCILAB (Kothe, C. A., & Makeig, S., 2013): Open-source MatLab toolbox for brain-computer interface. Includes a wide variety of machine learning algorithms applied to EEG and ERP data.
Resources: YouTube video serious by Kothe.
MobiLab (Ojeda, A., Bigdely-Shamlo, N., & Makeig, S., 2014): MatLab-based toolbox for processing complex, concurrent, multi-modal, and multi-rate data streams.
PyReimann (Congedo, M., Barachant, A., & Bhatia, R., 2017 for review): Free and open-source python package for biosignals classification with Riemannian Geometry machine learning applications.
Regression-based ERP/ERF analysis (Smith, N. J., & Kutas, M., 2015a; Smith, N. J., & Kutas, M., 2015b; Burns, M. D., Bigdely-Shamlo, N., Smith, N. J., Kreutz-Delgado, K., & Makeig, S., 2013): rERP/rERF analysis is a technique for using regression to estimate ERP/ERF waveforms from brainwave data recorded using EEG or MEG.
1. rERPy: Python, can calculate arbitrary rERP/rERF designs with or without overlap correction, powerful tools for describing and selecting events and creating design matrices. Not yet documented.
2. LIMO: EEGLAB plugin, may not follow all recommendations in the above manuscripts by default but with some work I believe should be able to perform rERP/rERF regression analyses using arbitrary design matrices. No support for overlap correction.
3. rERP toolbox: EEGLAB plugin, limited tools for defining design matrices, can compute both rERP/rERFs and rERSPs with overlap correction.
Burns, M. D., Bigdely-Shamlo, N., Smith, N. J., Kreutz-Delgado, K., & Makeig, S. (2013). Comparison of Averaging and Regression Techniques for Estimating Event Related Potentials. In IEEE Engineering in Biology and Medicine Conference, Osaka, Japan. (PDF)
Cohen, M. X. (2014). Analyzing neural time series data: theory and practice. MIT Press.
Congedo, M., Barachant, A., & Bhatia, R. (2017). Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review. Brain-Computer Interfaces, 1-20.
Delorme A, Makeig S, EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis (2004) Journal of Neuroscience Methods 134:9-21. See also: http://sccn.ucsd.edu/wiki/EEGLAB/
Dien, J. (2010). The ERP PCA Toolkit: An open source program for advanced statistical analysis of event-related potential data. Journal of neuroscience methods, 187(1), 138-145.
Kothe, C. A., & Makeig, S. (2013). BCILAB: a platform for brain–computer interface development. Journal of neural engineering, 10(5), 056014.
Lopez-Calderon, J., & Luck, S. J. (2014). ERPLAB: an open-source toolbox for the analysis of event-related potentials. Frontiers in human neuroscience, 8.
Luck, S. J. (2014). An introduction to the event-related potential technique. MIT press.
Ojeda, A., Bigdely-Shamlo, N., & Makeig, S. (2014). MoBILAB: an open source toolbox for analysis and visualization of mobile brain/body imaging data. Frontiers in human neuroscience, 8.
Smith, N. J., & Kutas, M. (2015a). Regression-based estimation of ERP waveforms: I. The rERP framework. Psychophysiology, 52(2), 157-168.
Smith, N. J., & Kutas, M. (2015b). Regression-based estimation of ERP waveforms: II. Non-linear effects, overlap correction, and practical considerations. Psychophysiology, 52(2), 169-189.
This work was supported by the National Science Foundation Graduate Research Fellowship Program [DGE-2016231981 to J.E.G].