Computational testing for automated preprocessing: a Matlab toolbox to enable large scale electroencephalography data processing

Cowley, Benjamin Ultan; Korpela, Jussi; Torniainen, Jari

doi:10.7717/peerj-cs.108

Cited by 25 publications

(41 citation statements)

References 28 publications

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“…The Harvard Automated Processing Pipeline for EEG (HAPPE) (Gabard-Durnam et al, 2018) and the automatic pre-processing pipeline (APP) for EEG analysis (da Cruz et al, 2018) combine multiple methods into pipelines that additionally offer artifact correction using independent component analysis (ICA) and Multiple Artifact Rejection Algorithm (MARA, (Winkler et al, 2011) ). The Computational Testing for Automated Preprocessing (CTAP) toolbox (Cowley et al, 2017;Cowley and Korpela, 2018) has a similar goal but additionally offers functions to optimize the different preprocessing methods by allowing the user to compare different pipelines. The Batch Electroencephalography Automated Processing Platform (BEAPP, (Levin et al, 2018) ) reflects a platform for batch processing 4 of EEG that allows the creation of preprocessing pipelines with a variety of options that can be re-applied to new datasets, hence facilitating the exact methodological replication of studies.…”

Section: Introductionmentioning

confidence: 99%

Automagic: Standardized preprocessing of big EEG data

2019

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Electroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG recordings but mainly on account of methodological issues. In many cases, particularly in clinical, pediatric and aging populations, the EEG has a high degree of artifact contamination and the quality of EEG recordings often substantially differs between subjects. Although there exist a variety of standardized preprocessing methods to clean EEG from artifacts, currently there is no method to objectively quantify the quality of preprocessed EEG. This makes the commonly accepted procedure of excluding subjects from analyses due to exceeding contamination of artifacts highly subjective. As a consequence, P-hacking is fostered, the replicability of results is decreased, and it is difficult to pool data from different study sites. In addition, in large-scale studies, data are collected over years or even decades, requiring software that controls and manages the preprocessing of ongoing and dynamically growing studies. To address these challenges, we developed AUTOMAGIC, an open-source MATLAB toolbox that acts as wrapper to run currently available preprocessing methods and offers objective standardized quality assessment for growing studies. The software is compatible with the Brain Imaging Data Structure (BIDS) standard and hence facilitates data sharing. In the present paper we outline the functionality of AUTOMAGIC and examine the effect of applying combinations of methods on a sample of resting EEG data. This examination suggests that a applying a pipeline of algorithms to detect artifactual channels in combination with Multiple Artifact Rejection Algorithm (MARA), an independent component analysis (ICA)-based artifact correction method, is sufficient to reduce a large extent of artifacts.

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Section: Introductionmentioning

confidence: 99%

Automagic: Standardized preprocessing of big EEG data

2019

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“…ADJUST uses a different set of spatial and temporal features to identify bad components. Only Automagic and partially Computational Testing for Automated Preprocessing (CTAP) (Cowley, Korpela, and Torniainen 2017;Cowley and Korpela 2018) (only EOG) offer additional artifact correction methods, such as EOG regression and robust PCA.…”

Section: <Insert Table 1 Here>mentioning

confidence: 99%

“…The Harvard Automated Processing Pipeline for EEG (HAPPE) ) reflects a standardized pipeline that additionally offers artifact correction using Wavelet-enhanced independent component analysis (ICA), ICA and Multiple Artifact Rejection Algorithm (MARA, Winkler, Haufe, and Tangermann 2011;Winkler et al 2014). The Computational Testing for Automated Preprocessing (CTAP) toolbox (Cowley, Korpela, and Torniainen 2017;Cowley and Korpela 2018) has a similar goal but additionally offers functions to optimize the different preprocessing methods by allowing the user to compare different pipelines. More recently, the Batch Electroencephalography Automated Processing Platform (BEAPP, Levin et al 2018) reflects a platform for batch processing of EEG that allows the creation of preprocessing pipelines with a variety of options that can be re-applied to new datasets, hence facilitating the exact methodological replication of studies.…”

Section: Introductionmentioning

confidence: 99%

“…The recently introduced pipelines HAPPE ( Gabard-Durnam et al 2018) and CTAP (Cowley, Korpela, and Torniainen 2017;Cowley and Korpela 2018) offer standardized metrics of the data quality allowing objective classification of datasets. For HAPPE, quality metrics are the number of interpolated channels, the number of epochs that survive epoch-wise artifact rejection after preprocessing, the number of independent components (IC) that are rejected as artifact components, and the retained variance of the EEG data after IC rejection.…”

Section: Introductionmentioning

confidence: 99%

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Automagic: Standardized Preprocessing of Big EEG Data

Pedroni

Bahreini

Langer

2018

Preprint

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Electroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG recordings but mainly on account of methodological issues. In many cases, particularly in clinical, pediatric and aging populations, the EEG has a high degree of artifact contamination and the quality of EEG recordings often substantially differs between subjects. Although there exists a variety of standardized preprocessing methods to clean EEG from artifacts, currently there is no method to objectively quantify the quality of preprocessed EEG.This makes the commonly accepted procedure of excluding subjects from analyses due to exceeding contamination of artifacts highly subjective. As a consequence, P-hacking is fostered, the replicability of results is decreased, and it is difficult to pool data from different study sites. In addition, in largescale studies, data are collected over years or even decades, requiring software that controls and manages the preprocessing of ongoing and dynamically growing studies. To address these challenges, we developed Automagic, an open-source MATLAB toolbox that acts as a wrapper to run currently available preprocessing methods and offers objective standardized quality assessment for growing studies. The software is compatible with the Brain Imaging Data Structure (BIDS) standard and hence facilitates data sharing. In the present paper we outline the functionality of Automagic and examine the effect of applying combinations of methods on a sample of resting EEG data. This examination suggests that applying a pipeline of algorithms to detect artifactual channels in combination with Multiple Artifact Rejection Algorithm (MARA), an independent component analysis (ICA)-based artifact correction method, is sufficient to reduce a large extent of artifacts.

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“…The data was preprocessed using batch scripts powered by CTAP (B. U. Cowley, Korpela, & Torniainen, 2017), in the following steps:…”

Section: Analysis 231 Data Preprocessingmentioning

confidence: 99%

The PSICAT protocol - Primed Subjective-Illusory-Contour Attention Task for studying integrated functional cognitive basis of sustained attention

Cowley¹

2017

Preprint

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Understanding and measuring sustained attention is important for basic research and applications. Measurement and testing with computerised continuous performance tests is common. When studying the neural correlates of attention, such tests aim to isolate the construct of sustained attention. However, in any ecologically-valid context, sustained attention integrates with other executive functions and depends on lower level perceptual processing. Examination of these interactions is not well-served by existing options, and hence I propose a novel protocol to probe higher cognition and its neural correlates: the Primed Subjective Illusory Contour Attention Task (PSICAT). PSICAT tackles two important aspects of ecological attention: (a) successful attending may require inhibition of interference; (b) natural target stimuli are complex and often noisy. I describe a validation experiment measuring healthy participants with high-resolution electroencephalography. Behavioural and brain-imaging results validate the key hypotheses for the functionality of PSICAT. Additionally, I demonstrate the novel result that task-irrelevant incongruency of an intra-modal distractor prime can evoke stronger behavioural and neural responses than the task-relevant stimulus condition. Finally, the protocol is available as an open-source code repository at the following url, allowing researchers to reuse and adapt it to their particular requirements. https://github.com/zenBen/Kanizsa_Prime/

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Computational testing for automated preprocessing: a Matlab toolbox to enable large scale electroencephalography data processing

Cited by 25 publications

References 28 publications

Automagic: Standardized preprocessing of big EEG data

Automagic: Standardized preprocessing of big EEG data

Automagic: Standardized Preprocessing of Big EEG Data

The PSICAT protocol - Primed Subjective-Illusory-Contour Attention Task for studying integrated functional cognitive basis of sustained attention

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