BIDS-EEG: an extension to the Brain Imaging Data Structure (BIDS) Specification for electroencephalography

Pernet, Cyril; Appelhoff, Stefan; Flandin, Guillaume; Phillips, Christophe; Delorme, Arnaud; Oostenveld, Robert

doi:10.31234/osf.io/63a4y

Cited by 35 publications

(37 citation statements)

References 16 publications

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“…Classification of EEG signals (136) Improvement of processing tools (13) Generation of data (7) Clinical ( Figure 4: Focus of the studies. The number of papers that fit in a category is showed in brackets for each category.…”

Section: + Eeg Studiesmentioning

confidence: 99%

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Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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Context. Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question.Objective. In this work, we review 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, braincomputer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations.Methods. Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to 1) the data, 2) the preprocessing methodology, 3) the DL design choices, 4) the results, and 5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends. * The first two authors contributed equally to this work.Significance. To help the community progress and share work more effectively, we provide a list of recommendations for future studies. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly.

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Section: + Eeg Studiesmentioning

confidence: 99%

“…FAIR neuroscience [196] and the Brain Imaging Data Structure (BIDS) [56] both provide guidelines and standards on how to acquire, organize and share data and code. BIDS extensions specific to EEG [136] and MEG [119] were also recently proposed. Moreover, open source software toolboxes are available to perform DL experiments on EEG.…”

Section: Reproducibilitymentioning

confidence: 99%

Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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“…In addition, Automagic offers the export of a project into a brain imaging data structure (BIDS) compatible structure to facilitate data sharing. BIDS is a data sharing standard that has been originally developed for MRI data but now also encompasses other modalities, including an extension for EEG (Pernet et al, 2018a) . The BIDS reflects a systematic way to organize data in a folder structure with dedicated names.…”

Section: Bids Integrationmentioning

confidence: 99%

“…The principled way of data sharing has been successfully adopted in the domain of MRI data with the introduction of the Brain Imaging Data Structure (BIDS), the emerging standard for the organisation of neuroimaging data (Gorgolewski et al, 2016) . Various extensions of the BIDS format (including extensions for EEG data (Pernet et al, 2018a) ) have been proposed ( https://bids.neuroimaging.io ) that not only provide a standard for the respective data modality but moreover facilitate the integration between data of different modalities (e.g. simultaneous fMRI and EEG recordings).…”

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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“…(1) A data repository structure allowing for a centrally pooled data set of independently collected cohorts of infant sibling EEG data; (2) The adoption of the Brain Imaging Data Structure (BIDS; Gorgolewski et al 2016) extension for EEG (BIDS-EEG; Pernet et al 2018) that harmonizes the storage of EEG acquisition parameters, as well as experimental and individual difference variables in a common framework across pooled projects; (3) Implementation of the Lossless signal processing pipeline (https://github.com/BUCANL/bids_lossless_eeg) that produces a common EEG data state that both maximizes signal isolation and minimizes data loss by applying quality control measures on each recording session in the data set. The Lossless pipeline performs several data quality assessment procedures, Adaptive Mixture Independent Component Analysis (AMICA), and signal property annotation.…”

Section: Introductionmentioning

confidence: 99%

EEG-IP: an international infant EEG data integration platform for the study of risk and resilience in autism and related conditions

Noordt

Desjardins

Huberty

et al. 2020

Mol Med

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Background: Establishing reliable predictive and diganostic biomarkers of autism would enhance early identification and facilitate targeted intervention during periods of greatest plasticity in early brain development. High impact research on biomarkers is currently limited by relatively small sample sizes and the complexity of the autism phenotype. Methods: EEG-IP is an International Infant EEG Data Integration Platform developed to advance biomarker discovery by enhancing the large scale integration of multi-site data. Currently, this is the largest multi-site standardized dataset of infant EEG data. Results: First, multi-site data from longitudinal cohort studies of infants at risk for autism was pooled in a common repository with 1382 EEG longitudinal recordings, linked behavioral data, from 432 infants between 3-to 36-months of age. Second, to address challenges of limited comparability across independent recordings, EEG-IP applied the Brain Imaging Data Structure (BIDS)-EEG standard, resulting in a harmonized, extendable, and integrated data state. Finally, the pooled and harmonized raw data was preprocessed using a common signal processing pipeline that maximizes signal isolation and minimizes data reduction. With EEG-IP, we produced a fully standardized data set, of the pooled, harmonized, and pre-processed EEG data from multiple sites. Conclusions: Implementing these integrated solutions for the first time with infant data has demonstrated success and challenges in generating a standardized multi-site data state. The challenges relate to annotation of signal sources, time, and ICA analysis during pre-processing. A number of future opportunities also emerge, including validation of analytic pipelines that can replicate existing findings and/or test novel hypotheses.

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BIDS-EEG: an extension to the Brain Imaging Data Structure (BIDS) Specification for electroencephalography

Cited by 35 publications

References 16 publications

Deep learning-based electroencephalography analysis: a systematic review

Deep learning-based electroencephalography analysis: a systematic review

Automagic: Standardized preprocessing of big EEG data

EEG-IP: an international infant EEG data integration platform for the study of risk and resilience in autism and related conditions

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