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

Real-world brain imaging by EEG requires accurate annotation of complex subject-environment interactions in event-rich tasks and paradigms. This paper describes the evolution of the Hierarchical Event Descriptor (HED) system for systematically describing both laboratory and real-world events. HED version 2, first described here, provides the semantic capability of describing a variety of subject and environmental states. HED descriptions can include stimulus presentation events on screen or in virtual worlds, experimental or spontaneous events occurring in the real world environment, and events experienced via one or multiple sensory modalities. Furthermore, HED 2 can distinguish between the mere presence of an object and its actual (or putative) perception by a subject. Although the HED framework has implicit ontological and linked data representations, the user-interface for HED annotation is more intuitive than traditional ontological annotation. We believe that hiding the formal representations allows for a more user-friendly interface, making consistent, detailed tagging of experimental, and real-world events possible for research users. HED is extensible while retaining the advantages of having an enforced common core vocabulary. We have developed a collection of tools to support HED tag assignment and validation; these are available at hedtags.org. A plug-in for EEGLAB (sccn.ucsd.edu/eeglab), CTAGGER, is also available to speed the process of tagging existing studies

Hierarchical Event Descriptor library schema for EEG data annotation

Standardizing terminology to describe electrophysiological events can improve both clinical care and computational research. Sharing data enriched by such standardized terminology can support advances in neuroscientific data exploration, from single-subject to mega-analysis. Machine readability of electrophysiological event annotations is essential for performing such analyses efficiently across software tools and packages. Hierarchical Event Descriptors (HED) provide a framework for describing events in neuroscience experiments. HED library schemas extend the standard HED schema vocabulary to include specialized vocabularies, such as standardized clinical terms for electrophysiological events. The Standardized Computer-based Organized Reporting of EEG (SCORE) defines terms for annotating EEG events, including artifacts. This study makes SCORE machine-readable by incorporating it into a HED library schema. We demonstrate the use of the HED-SCORE library schema to annotate events in example EEG data stored in Brain Imaging Data Structure (BIDS) format. Clinicians and researchers worldwide can now use the HED-SCORE library schema to annotate and then compute on electrophysiological data obtained from the human brain.Comment: 22 pages, 5 figure

EEG-BIDS, an extension to the brain imaging data structure for electroencephalography

The Brain Imaging Data Structure (BIDS) project is a rapidly evolving effort in the human brain imaging research community to create standards allowing researchers to readily organize and share study data within and between laboratories. Here we present an extension to BIDS for electroencephalography (EEG) data, EEG-BIDS, along with tools and references to a series of public EEG datasets organized using this new standard

SEREEGA: Simulating Event-Related EEG Activity

Abstract Electroencephalography (EEG) is a popular method to monitor brain activity, but it can be difficult to evaluate EEG-based analysis methods because no ground-truth brain activity is available for comparison. Therefore, in order to test and evaluate such methods, researchers often use simulated EEG data instead of actual EEG recordings, ensuring that it is known beforehand which e ects are present in the data. As such, simulated data can be used, among other things, to assess or compare signal processing and machine learn-ing algorithms, to model EEG variabilities, and to design source reconstruction methods. In this paper, we present SEREEGA, short for Simulating Event-Related EEG Activity . SEREEGA is a MATLAB-based open-source toolbox dedicated to the generation of sim-ulated epochs of EEG data. It is modular and extensible, at initial release supporting ve different publicly available head models and capable of simulating multiple different types of signals mimicking brain activity. This paper presents the architecture and general work ow of this toolbox, as well as a simulated data set demonstrating some of its functions. Highlights Simulated EEG data has a known ground truth, which can be used to validate methods. We present a general-purpose open-source toolbox to simulate EEG data. It provides a single framework to simulate many different types of EEG recordings. It is modular, extensible, and already includes a number of head models and signals. It supports noise, oscillations, event-related potentials, connectivity, and more

Mobile Brain and Body Imaging during Walking Motor Tasks

Mobile brain and body imaging (MoBI) presents new and promising methods for moving traditional research studies out of a controlled laboratory and into the real world. Most current neuroimaging techniques require subjects to be stationary in laboratory settings because of both hardware and software limitations. Recent developments in mobile brain imaging have utilized Electroencephalography (EEG) in conjunction with advanced signal processing techniques such as Independent Component Analysis (ICA) to overcome these obstacles and study humans doing complex tasks in non-traditional environments. In my first study, I used high density EEG to examine the cortical dynamics of subjects walking on a split-belt treadmill with legs moving independently of each other at different speeds to investigate how humans adapt to novel perturbations. I found significantly increased low and high frequency spectral power across all sensorimotor and parietal neural sources during split-belt adaptation compared to normal walking, which provides insight into the brain areas and patterns used to accommodate locomotor adaptation. In my second study I combined multi-modal sensing and biometric devices including EEG, eye tracking, heart rate, accelerometers, and salivary cortisol into a portable setup that subjects wore indoors on a treadmill using virtual reality as well as outdoors in a public arboretum. Subjects walked for 1 hour each indoors and outdoors while completing a free viewing visual search oddball task in virtual reality and in real life. I reported on the methods for how to set this experiment up, synchronize all data, and standardize the data in order to make it usable as an open access dataset that has been made available to the public online. My third study used this data set to examine the P300 event-related potential response during both indoors in virtual reality and outdoors in the arboretum. I found a significantly increased amplitude response between 250 to 400 ms across the centro-parietal electrodes that distinguished target flags from distractor flags during visual search for both indoor and outdoor environments. And finally, for my fourth study I used the same data set to look at the behavioral and neural correlates associated with gait dynamics when subjects walked indoors on a treadmill vs outdoors in variable terrain while also doing the visual search task. I found significant EEG power differences across multiple neural sources that showed increased spectral fluctuations throughout the gait cycle when subjects walked outdoors compared to indoors on a treadmill. The collective studies in this dissertation present new ways of using mobile brain and body imaging devices to expand our knowledge of the neural dynamics involved in humans moving in complex ways and in variable environments outside of traditional laboratories.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147691/1/ghanada_1.pd

ECHO Information sharing models

As part of the ECHO project, the Early Warning System (EWS) is one of four technologies under development. The E-EWS will provide the capability to share information to provide up to date information to all constituents involved in the E-EWS. The development of the E-EWS will be rooted in a comprehensive review of information sharing and trust models from within the cyber domain as well as models from other domains

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe