The ERP PCA Toolkit: An open source program for advanced statistical analysis of event-related potential data

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Abstract

This article presents an open source Matlab program, the ERP PCA (EP) Toolkit, for facilitating the multivariate decomposition and analysis of event-related potential data. This program is intended to supplement existing ERP analysis programs by providing functions for conducting artifact correction, robust averaging, referencing and baseline correction, data editing and visualization, principal components analysis, and robust inferential statistical analysis. This program subserves three major goals: (1) optimizing analysis of noisy data, such as clinical or developmental; (2) facilitating the multivariate decomposition of ERP data into its constituent components; (3) increasing the transparency of analysis operations by providing direct visualization of the corresponding waveforms.

Section snippets

Requirements

The EP Toolkit has the following requirements to use. The Toolkit itself is free and available via download (https://sourceforge.net/projects/erppcatoolkit/). Those interested can also join the mailing list (https://lists.sourceforge.net/lists/listinfo/erppcatoolkit-support) to be alerted when new versions are posted. It is necessary to have a license for Matlab, and it has been tested under both OS X and Windows XP using Matlab 2006 through 2009b. It is recommended that the computer have a

Preparation of the data

The primary goal for this first section is to provide a sense to potential users of what is required to use the EP Toolkit to prepare the data. The nature of the procedures themselves have largely been reported on elsewhere and just the appropriate citations will be provided. Data preparation in turn consists of artifact correction and robust averaging. Functions are accessed via the Start Pane (Fig. 1):

The Toolkit can automatically eliminate both blink artifacts and movement artifacts from

Conclusion

In conclusion, the EP Toolkit is designed to optimize analysis of noisy data by providing automatic artifact correction, robust averaging, and robust inferential statistics. The Toolkit facilitates multivariate decomposition of ERP data by providing simple functions for applying PCA and ICA for both one-step and two-step procedures, visualizing the results by reconstructing the factor waveforms, and applying inferential statistics. Finally, the Toolkit increases the transparency of analysis by

Acknowledgements

This work was supported in part by NASA Grant SA23-06-015. Thanks to Dennis Molfese for his unfailing backing. Thanks also to the two anonymous reviewers for their helpful comments.

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