Computational testing for automated preprocessing: a matlab toolbox for better electroencephalography data processing
- Published
- Accepted
- Subject Areas
- Bioinformatics, Brain-Computer Interface
- Keywords
- computation, testing, automation, preprocessing, EEGLAB, electroencephalography, signal processing
- Copyright
- © 2016 Cowley et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. Computational testing for automated preprocessing: a matlab toolbox for better electroencephalography data processing. PeerJ Preprints 4:e2140v1 https://doi.org/10.7287/peerj.preprints.2140v1
Abstract
EEG is a rich source of information regarding brain functioning, and is the most lightweight and affordable method of brain imaging. However, the pre-processing of EEG data is quite complicated and most existing tools present the experimenter with a large choice of methods for analysis, but no framework for method comparison to choose an optimal approach. Additionally, many tools still require a high degree of manual decision making for, e.g. the classification of artefacts in channels, epochs or segments. This introduces excessive subjectivity, is slow, and is not reproducible. Batching and well-designed automation can help to regularise EEG preprocessing, and thus minimise human effort, subjectivity, and consequent error. The Computational Testing for Automated Preprocessing (CTAP) toolbox facilitates: i) batch processing that is easy for experts and novices alike; ii) testing and comparison of automated methods. CTAP uses the existing data structure and functions from the well-known EEGLAB tool, based on Matlab, and produces extensive quality control outputs.
Author Comment
This is a submission to PeerJ Computer Science for review.