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

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).

Automagic: Standardized preprocessing of big EEG data

“…The BIDS reflects a systematic way to organize data in a folder structure with dedicated names. An alternative standard to EEG BIDS following the same goal of enabling data sharing is the EEG Study Schema (ESS) (Bigdely-Shamlo et al, 2016) . We have decided to implement BIDS EEG (instead of the ESS) because the BIDS EEG can be readily combined to other BIDS extensions such as an extension for eye tracking, or fMRI that all follow the same basic structure.…”

Automated EEG mega-analysis II: Cognitive aspects of event related features

“…We used several open-source tools that we have developed to do the automated preprocessing. To facilitate preprocessing we organized each study by placing it in a containerized format using the EEG Study Schema (ESS) as described in Bigdely-Shamlo et al (Bigdely-Shamlo et al, 2016b). ESS, which is part of the BigEEG technology stack (BigEEG Workflow, 2018), allows specification, in machine-readable format, of channel labels and locations as well as meta-data such as subject and task information about each recording.…”

Hierarchical Event Descriptors (HED): Semi-Structured Tagging for Real-World Events in Large-Scale EEG

“…So far, we have tagged millions of events across 1860 datasets from 22 EEG studies. We are also in the process of converting the publicly available HED 1.0 annotated datasets from headit.org to HED 2.0 and ESS 2.0 (Bigdely-Shamlo et al, 2016 ). Figure 3 shows a visual representation of the relative numbers of occurrences of events in several major categories.…”

“…Here, we introduce HED 2.0, a considerable extension of the HED 1.0 system to allow descriptions of the much larger range of events of interest contained in real and virtual world EEG experiments. HED 2.0, combined with our proposed EEG Study Schema (ESS) and containerization tools (Bigdely-Shamlo et al, 2016 ), create a tool and standards ecosystem that make it possible to store and interrogate data in a large or small collection of similar or diverse EEG studies. The goal of the HED/ESS system is to allow researchers to obtain new information about brain dynamics supporting human experience and behavior not easily obtainable from any one experiment by exploiting information still buried in the accumulating stores of carefully collected EEG and related data.…”