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

Nima Bigdely-Shamlo,Kay A. Robbins,Scott Makeig

doi:10.3389/fninf.2016.00007

Nima Bigdely-Shamlo, Kay A. Robbins + Show 1 more

Open Access

https://doi.org/10.3389/fninf.2016.00007

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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).

Highlights

Traditional laboratory EEG studies typically collect data from a relatively small number of subjects in a controlled environment, using paradigms including a limited number of instances of clearly defined experimental event types, subject tasks, and task conditions
Collected datasets from 10s of 1000s of such experiment sessions currently reside on disks in individual investigators’ laboratories. They become unusable once the many details needed to process them meaningfully have been lost or forgotten. This data, collected at great expense and effort, could be analyzed collectively using rapidly evolving statistical data modeling methods to shed new light on the cortical brain dynamics that support a range of cognitive abilities in both healthy and unhealthy subjects
The first step is to use the preliminary XML file to create a level1Study object in MATLAB: obj=level1Study('D:\ BCIT\ study_description.xml'); to make sure the manifest contains all the necessary information and is self-consistent the researcher validates the manifest by calling the EEG Study Schema (ESS) validation function: obj=obj.validate; The validation function runs more than 30 checks and fixes minor issues such as missing UUIDs for data recordings

Summary

Introduction

Traditional laboratory EEG studies typically collect data from a relatively small number of subjects in a controlled environment, using paradigms including a limited number of instances of clearly defined experimental event types, subject tasks, and task conditions. The tasks required to prepare EEG studies for data sharing and large-scale analysis include: (1) describing the experimental paradigm and experimental events of interest in a standardized manner; (2) encapsulating study-level metadata (subject groups, association of files, subject and sessions, data provenance, etc.) in a standardized manner; (3) providing access to online resources allowing users to find and download the data.

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Conclusion