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. 2016 Mar 8:10:7.
doi: 10.3389/fninf.2016.00007. eCollection 2016.

Preparing Laboratory and Real-World EEG Data for Large-Scale Analysis: A Containerized Approach

Affiliations

Preparing Laboratory and Real-World EEG Data for Large-Scale Analysis: A Containerized Approach

Nima Bigdely-Shamlo et al. Front Neuroinform. .

Abstract

Large-scale analysis of EEG and other physiological measures promises new insights into brain processes and more accurate and robust brain-computer interface models. However, the absence of standardized vocabularies for annotating events in a machine understandable manner, the welter of collection-specific data organizations, the difficulty in moving data across processing platforms, and the unavailability of agreed-upon standards for preprocessing have prevented large-scale analyses of EEG. Here we describe a "containerized" approach and freely available tools we have developed to facilitate the process of annotating, packaging, and preprocessing EEG data collections to enable data sharing, archiving, large-scale machine learning/data mining and (meta-)analysis. The EEG Study Schema (ESS) comprises three data "Levels," each with its own XML-document schema and file/folder convention, plus a standardized (PREP) pipeline to move raw (Data Level 1) data to a basic preprocessed state (Data Level 2) suitable for application of a large class of EEG analysis methods. Researchers can ship a study as a single unit and operate on its data using a standardized interface. ESS does not require a central database and provides all the metadata data necessary to execute a wide variety of EEG processing pipelines. The primary focus of ESS is automated in-depth analysis and meta-analysis EEG studies. However, ESS can also encapsulate meta-information for the other modalities such as eye tracking, that are increasingly used in both laboratory and real-world neuroimaging. ESS schema and tools are freely available at www.eegstudy.org and a central catalog of over 850 GB of existing data in ESS format is available at studycatalog.org. These tools and resources are part of a larger effort to enable data sharing at sufficient scale for researchers to engage in truly large-scale EEG analysis and data mining (BigEEG.org).

Keywords: BCI; EEG; large scale analysis; neuroinformatics.

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Figures

FIGURE 1
FIGURE 1
EEG Study Schema (ESS) processing stages and tools used to transform data into successive containerized ESS standard level formats (Delorme and Makeig, 2004; Kothe and Makeig, 2013).
FIGURE 2
FIGURE 2
EEG Study Schema Standardized Data Level 1 folder and file structure (blue: folder, green: files).
FIGURE 3
FIGURE 3
EEG Study Schema Standardized Data Level 2 folder and file structure (blue: folder, green: files, magenta: additional data).
FIGURE 4
FIGURE 4
Sample automated HTML report generated in the Chrome web browser by the included XSLT styling sheet from the study_description.xml manifest file of an ESS Standardized Level 1 container.
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References

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