Computational Testing for Automated Preprocessing 2: Practical Demonstration of a System for Scientific Data-Processing Workflow Management for High-Volume EEG
Existing tools for the preprocessing of EEG data provide a large choice of methods to suitably prepare and analyse a given dataset. Yet it remains a challenge for the average user to integrate methods for batch processing of the increasingly large datasets of modern research, and compare methods to...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-04-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/article/10.3389/fnins.2018.00236/full |
| _version_ | 1818450849299431424 |
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| author | Benjamin U. Cowley Benjamin U. Cowley Jussi Korpela |
| author_facet | Benjamin U. Cowley Benjamin U. Cowley Jussi Korpela |
| author_sort | Benjamin U. Cowley |
| collection | DOAJ |
| description | Existing tools for the preprocessing of EEG data provide a large choice of methods to suitably prepare and analyse a given dataset. Yet it remains a challenge for the average user to integrate methods for batch processing of the increasingly large datasets of modern research, and compare methods to choose an optimal approach across the many possible parameter configurations. Additionally, many tools still require a high degree of manual decision making for, e.g., the classification of artifacts in channels, epochs or segments. This introduces extra subjectivity, is slow, and is not reproducible. Batching and well-designed automation can help to regularize EEG preprocessing, and thus reduce 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 preprocessing methods. Here we demonstrate the application of CTAP to high-resolution EEG data in three modes of use. First, a linear processing pipeline with mostly default parameters illustrates ease-of-use for naive users. Second, a branching pipeline illustrates CTAP's support for comparison of competing methods. Third, a pipeline with built-in parameter-sweeping illustrates CTAP's capability to support data-driven method parameterization. CTAP extends the existing functions and data structure from the well-known EEGLAB toolbox, based on Matlab, and produces extensive quality control outputs. CTAP is available under MIT open-source licence from https://github.com/bwrc/ctap. |
| first_indexed | 2024-12-14T20:57:50Z |
| format | Article |
| id | doaj.art-caa0733f4b8c45ecafb611619f8901f8 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-14T20:57:50Z |
| publishDate | 2018-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-caa0733f4b8c45ecafb611619f8901f82022-12-21T22:47:39ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2018-04-011210.3389/fnins.2018.00236325745Computational Testing for Automated Preprocessing 2: Practical Demonstration of a System for Scientific Data-Processing Workflow Management for High-Volume EEGBenjamin U. Cowley0Benjamin U. Cowley1Jussi Korpela2Cognitive Science, Department of Digital Humanities, University of Helsinki, Helsinki, FinlandCognitive Brain Research Unit, Department of Psychology and Logopedics, University of Helsinki, Helsinki, FinlandDigitalization, Finnish Institute of Occupational Health, Helsinki, FinlandExisting tools for the preprocessing of EEG data provide a large choice of methods to suitably prepare and analyse a given dataset. Yet it remains a challenge for the average user to integrate methods for batch processing of the increasingly large datasets of modern research, and compare methods to choose an optimal approach across the many possible parameter configurations. Additionally, many tools still require a high degree of manual decision making for, e.g., the classification of artifacts in channels, epochs or segments. This introduces extra subjectivity, is slow, and is not reproducible. Batching and well-designed automation can help to regularize EEG preprocessing, and thus reduce 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 preprocessing methods. Here we demonstrate the application of CTAP to high-resolution EEG data in three modes of use. First, a linear processing pipeline with mostly default parameters illustrates ease-of-use for naive users. Second, a branching pipeline illustrates CTAP's support for comparison of competing methods. Third, a pipeline with built-in parameter-sweeping illustrates CTAP's capability to support data-driven method parameterization. CTAP extends the existing functions and data structure from the well-known EEGLAB toolbox, based on Matlab, and produces extensive quality control outputs. CTAP is available under MIT open-source licence from https://github.com/bwrc/ctap.http://journal.frontiersin.org/article/10.3389/fnins.2018.00236/fullEEGelectroencephalographyEEGLABscientific workflow systemworkflow managementcomputational testing |
| spellingShingle | Benjamin U. Cowley Benjamin U. Cowley Jussi Korpela Computational Testing for Automated Preprocessing 2: Practical Demonstration of a System for Scientific Data-Processing Workflow Management for High-Volume EEG Frontiers in Neuroscience EEG electroencephalography EEGLAB scientific workflow system workflow management computational testing |
| title | Computational Testing for Automated Preprocessing 2: Practical Demonstration of a System for Scientific Data-Processing Workflow Management for High-Volume EEG |
| title_full | Computational Testing for Automated Preprocessing 2: Practical Demonstration of a System for Scientific Data-Processing Workflow Management for High-Volume EEG |
| title_fullStr | Computational Testing for Automated Preprocessing 2: Practical Demonstration of a System for Scientific Data-Processing Workflow Management for High-Volume EEG |
| title_full_unstemmed | Computational Testing for Automated Preprocessing 2: Practical Demonstration of a System for Scientific Data-Processing Workflow Management for High-Volume EEG |
| title_short | Computational Testing for Automated Preprocessing 2: Practical Demonstration of a System for Scientific Data-Processing Workflow Management for High-Volume EEG |
| title_sort | computational testing for automated preprocessing 2 practical demonstration of a system for scientific data processing workflow management for high volume eeg |
| topic | EEG electroencephalography EEGLAB scientific workflow system workflow management computational testing |
| url | http://journal.frontiersin.org/article/10.3389/fnins.2018.00236/full |
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