Hierarchical Event Descriptor library schema for EEG data annotation.

Standardizing terminology to annotate electrophysiological events can improve both computational research and clinical care. Enriching data with standard terms facilitates data exploration, from case studies to mega-analyses. The machine readability of such electrophysiological event annotations is essential for performing automated analyses.

HED LANG - A Hierarchical Event Descriptors library extension for annotation of language cognition experiments.

Experimental design in language cognition research often involves presenting language material while measuring associated behavior and/or neural activity. To make the collected data easily and fully analyzable by both the original data authors and others, it is important to have detailed information about the stimulus presentation events, including the nature and properties of the presented stimuli, using a common vocabulary and syntax. We present HED LANG, a library extension of the Hierarchical Event Descriptors (HED) event annotation schema for time series behavioral and neuroimaging data.

Events in context-The HED framework for the study of brain, experience and behavior.

The brain is a complex dynamic system whose current state is inextricably coupled to awareness of past, current, and anticipated future threats and opportunities that continually affect awareness and behavioral goals and decisions. Brain activity is driven on multiple time scales by an ever-evolving flow of sensory, proprioceptive, and idiothetic experience. Neuroimaging experiments seek to isolate and focus on some aspect of these complex dynamics to better understand how human experience, cognition, behavior, and health are supported by brain activity.

Building FAIR Functionality: Annotating Events in Time Series Data Using Hierarchical Event Descriptors (HED).

Human electrophysiological and related time series data are often acquired in complex, event-rich environments. However, the resulting recorded brain or other dynamics are often interpreted in relation to more sparsely recorded or subsequently-noted events. Currently a substantial gap exists between the level of event description required by current digital data archiving standards and the level of annotation required for successful analysis of event-related data across studies, environments, and laboratories.

Capturing the nature of events and event context using hierarchical event descriptors (HED).

Event-related data analysis plays a central role in EEG and MEG (MEEG) and other neuroimaging modalities including fMRI. Choices about which events to report and how to annotate their full natures significantly influence the value, reliability, and reproducibility of neuroimaging datasets for further analysis and meta- or mega-analysis. A powerful annotation strategy using the new third-generation formulation of the Hierarchical Event Descriptors (HED) framework and tools (hedtags.

How Sensitive Are EEG Results to Preprocessing Methods: A Benchmarking Study.

Although several guidelines for best practices in EEG preprocessing have been released, even studies that strictly adhere to those guidelines contain considerable variation in the ways that the recommended methods are applied. An open question for researchers is how sensitive the results of EEG analyses are to variations in preprocessing methods and parameters. To address this issue, we analyze the effect of preprocessing methods on downstream EEG analysis using several simple signal and event-related measures.

Automated EEG mega-analysis I: Spectral and amplitude characteristics across studies.

Significant achievements have been made in the fMRI field by pooling statistical results from multiple studies (meta-analysis). More recently, fMRI standardization efforts have focused on enabling the joint analysis of raw fMRI data across studies (mega-analysis), with the hope of achieving more detailed insights. However, it has not been clear if such analyses in the EEG field are possible or equally fruitful.

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

We present the results of a large-scale analysis of event-related responses based on raw EEG data from 17 studies performed at six experimental sites associated with four different institutions. The analysis corpus represents 1,155 recordings containing approximately 7.8 million event instances acquired under several different experimental paradigms.

Single-Trial Classification of Disfluent Brain States in Adults Who Stutter.

Normal human speech requires precise coordination between motor planning and sensory processing. Speech disfluencies are common when children learn to talk, but usually abate with time. About 5% of children experience stuttering.

An 18-subject EEG data collection using a visual-oddball task, designed for benchmarking algorithms and headset performance comparisons.

This data note describes an 18-subject EEG (electroencephalogram) data collection from an experiment in which subjects performed a standard visual oddball task. Several research projects have used this data to test artifact detection, classification, transfer learning, EEG preprocessing, blink detection, and automated annotation algorithms. We are releasing the data in three formats to enable benchmarking of EEG algorithms in many areas.

EEG-Annotate: Automated identification and labeling of events in continuous signals with applications to EEG.

Background: In controlled laboratory EEG experiments, researchers carefully mark events and analyze subject responses time-locked to these events. Unfortunately, such markers may not be available or may come with poor timing resolution for experiments conducted in less-controlled naturalistic environments.

New Method: We present an integrated event-identification method for identifying particular responses that occur in unlabeled continuously recorded EEG signals based on information from recordings of other subjects potentially performing related tasks.

BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis.

Electroencephalography (EEG) offers a platform for studying the relationships between behavioral measures, such as blink rate and duration, with neural correlates of fatigue and attention, such as theta and alpha band power. Further, the existence of EEG studies covering a variety of subjects and tasks provides opportunities for the community to better characterize variability of these measures across tasks and subjects. We have implemented an automated pipeline (BLINKER) for extracting ocular indices such as blink rate, blink duration, and blink velocity-amplitude ratios from EEG channels, EOG channels, and/or independent components (ICs).

BLASST: Band Limited Atomic Sampling With Spectral Tuning With Applications to Utility Line Noise Filtering.

Objective: In this paper, we present and test a new method for the identification and removal of nonstationary utility line noise from biomedical signals.

Methods: The method, band limited atomic sampling with spectral tuning (BLASST), is an iterative approach that is designed to 1) fit nonstationarities in line noise by searching for best-fit Gabor atoms at predetermined time points, 2) self-modulate its fit by leveraging information from frequencies surrounding the target frequency, and 3) terminate based on a convergence criterion obtained from the same surrounding frequencies. To evaluate the performance of the proposed algorithm, we generate several simulated and real instances of nonstationary line noise and test BLASST along with alternative filtering approaches.

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.

PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG.

Independent component analysis (ICA) is a class of algorithms widely applied to separate sources in EEG data. Most ICA approaches use optimization criteria derived from temporal statistical independence and are invariant with respect to the actual ordering of individual observations. We propose a method of mapping real signals into a complex vector space that takes into account the temporal order of signals and enforces certain mixing stationarity constraints.

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

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 PREP pipeline: standardized preprocessing for large-scale EEG analysis.

The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision.

MOBBED: a computational data infrastructure for handling large collections of event-rich time series datasets in MATLAB.

Experiments to monitor human brain activity during active behavior record a variety of modalities (e.g., EEG, eye tracking, motion capture, respiration monitoring) and capture a complex environmental context leading to large, event-rich time series datasets.

Detecting alpha spindle events in EEG time series using adaptive autoregressive models.

Background: Rhythmic oscillatory activity is widely observed during a variety of subject behaviors and is believed to play a central role in information processing and control. A classic example of rhythmic activity is alpha spindles, which consist of short (0.5-2 s) bursts of high frequency alpha activity.

Classification of imperfectly time-locked image RSVP events with EEG device.

Classification based on EEG data in an RSVP experiment is considered. Although the latency in neural response relative to the stimulus onset time may be more realistically considered to vary across trials due to factors such as subject fatigue and environmental distractions, it is nevertheless assumed to be time-locked to the stimulus in most of the existing work as a means to alleviate the computational complexity. We consider here a more practical scenario that allows variation in response latency and develop a rigorous statistical formulation for modeling the uncertainty within the varying latency coupled with a likelihood ratio test (LRT) for classification.

DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.

Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series.