Neuroinformatics | NINE Lab

HBN-EEG: The FAIR implementation of the Healthy Brain Network (HBN) electroencephalography dataset

Thu, 03 Oct 2024 00:00:00 +0000

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Abstract

The Child Mind Institute (CMI) Healthy Brain Network (HBN) project has recorded phenotypic, behavioral, and neuroimaging data from ~5,000 children and young adults between the ages of 5 and 21. Here, we present “analysis-ready” data from its high-density (128-channel) electroencephalographic (EEG) recording sessions formatted as Brain Imaging Data Structure (BIDS) datasets (HBN-EEG) with behavioral and task-condition events annotated using Hierarchical Event Descriptors (HED), making it analysis-ready for many purposes, without ‘forensic’ search for unreported details. We also ensured individual data files data and event integrity and marked inconsistencies. HBN-EEG sessions include six tasks, three with no participant behavioral input and three including button press responses following task instructions. Openly available participant information includes age, gender, and four psychopathology dimensions (internalizing, externalizing, attention, and p-factor) derived from bifactor model of questionnaire data. Currently, HBN-EEG data from more than 3,000 participants is freely available on NEMAR (nemar.org) and OpenNeuro in the form of eleven dataset releases, with further dataset releases to follow. The HBN-EEG dataset is intended to support the development and validation of EEG analysis methods, including machine learning and deep learning approaches, and to facilitate the development of EEG-based biomarkers for psychiatric disorders.

The number of subjects per HBN-EEG dataset release

Citation

@ARTICLE{Shirazi2024-ye,
  title    = "{HBN}-{EEG}: The {FAIR} implementation of the Healthy Brain
              Network ({HBN}) electroencephalography dataset",
  author   = "Shirazi, Seyed Yahya and Franco, Alexandre and Hoffmann, Mauricio
              Scopel and Esper, Nathalia and Truong, Dung and Delorme, Arnaud
              and Milham, Michael and Makeig, Scott",
  journal  = "bioRxiv",
  pages    = "2024.10.03.615261",
  month    =  "3~" # oct,
  year     =  2024,
  doi      = "10.1101/2024.10.03.615261",
  language = "en"
}

The Lab Streaming Layer for Synchronized Multimodal Recording

Wed, 14 Feb 2024 00:00:00 +0000

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Abstract

Accurately recording the interactions of humans or other organisms with their environment or other agents requires synchronized data access via multiple instruments, often running independently using different clocks. Active, hardware-mediated solutions are often infeasible or prohibitively costly to build and run across arbitrary collections of input systems. The Lab Streaming Layer (LSL) offers a software-based approach to synchronizing data streams based on per-sample time stamps and time synchronization across a common LAN. Built from the ground up for neurophysiological applications and designed for reliability, LSL offers zero-configuration functionality and accounts for network delays and jitters, making connection recovery, offset correction, and jitter compensation possible. These features ensure precise, continuous data recording, even in the face of interruptions. The LSL ecosystem has grown to support over 150 data acquisition device classes as of Feb 2024, and establishes interoperability with and among client software written in several programming languages, including C/C++, Python, MATLAB, Java, C#, JavaScript, Rust, and Julia. The resilience and versatility of LSL have made it a major data synchronization platform for multimodal human neurobehavioral recording and it is now supported by a wide range of software packages, including major stimulus presentation tools, real-time analysis packages, and brain-computer interfaces. Outside of basic science, research, and development, LSL has been used as a resilient and transparent backend in scenarios ranging from art installations to stage performances, interactive experiences, and commercial deployments. In neurobehavioral studies and other neuroscience applications, LSL facilitates the complex task of capturing organismal dynamics and environmental changes using multiple data streams at a common timebase while capturing time details for every data frame.

The overall LSL design for synchronized data recording

Citation

@ARTICLE{Kothe2024-zm,
  title    = "The Lab Streaming Layer for Synchronized Multimodal Recording",
  author   = "Kothe, Christian and Shirazi, Seyed Yahya and Stenner, Tristan and
              Medine, David and Boulay, Chadwick and Crivich, Matthew I and
              Mullen, Tim and Delorme, Arnaud and Makeig, Scott",
  journal  = "bioRxiv",
  pages    = "2024.02.13.580071",
  month    =  feb,
  year     =  2024,
  language = "en"
}

More Reliable EEG Electrode Digitizing Methods Can Reduce Source Estimation Uncertainty, but Current Methods Already Accurately Identify Brodmann Areas

Wed, 06 Nov 2019 00:00:00 +0000

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Abstract

Electroencephalography (EEG) and source estimation can be used to identify brain areas activated during a task, which could offer greater insight on cortical dynamics. Source estimation requires knowledge of the locations of the EEG electrodes. This could be provided with a template or obtained by digitizing the EEG electrode locations. Operator skill and inherent uncertainties of a digitizing system likely produce a range of digitization reliabilities, which could affect source estimation and the interpretation of the estimated source locations. Here, we compared the reliabilities of five digitizing methods (ultrasound, structured-light 3D scan, infrared 3D scan, motion capture probe, and motion capture) and determined the relationship between digitization reliability and source estimation uncertainty, assuming other contributors to source estimation uncertainty were constant. We digitized a mannequin head using each method five times and quantified the reliability and validity of each method. We created five hundred sets of electrode locations based on our reliability results and applied a dipole fitting algorithm (DIPFIT) to perform source estimation. The motion capture method, which recorded the locations of markers placed directly on the electrodes had the best reliability with an average electrode variability of 0.001 cm. Then, in order of decreasing reliability were the method using a digitizing probe in the motion capture system, an infrared 3D scanner, a structured-light 3D scanner, and an ultrasound digitization system. Unsurprisingly, uncertainty of the estimated source locations increased with greater variability of EEG electrode locations and less reliable digitizing systems. If EEG electrode location variability was ∽1 cm, a single source could shift by as much as 2 cm. To help translate these distances into practical terms, we quantified Brodmann area accuracy for each digitizing method and found that the average Brodmann area accuracy for all digitizing methods was >80%. Using a template of electrode locations reduced the Brodmann area accuracy to ∽50%. Overall, more reliable digitizing methods can reduce source estimation uncertainty, but the significance of the source estimation uncertainty depends on the desired spatial resolution. For accurate Brodmann area identification, any of the digitizing methods tested can be used confidently.

The five digitizing methods tested in this study

Citation

@ARTICLE{Shirazi2019-im,
  title = "More Reliable {EEG} Electrode Digitizing Methods Can Reduce Source
        Estimation Uncertainty, but Current Methods Already Accurately
        Identify Brodmann Areas",
  author= "Shirazi, Seyed Yahya and Huang, Helen J",
  journal= "Front. Neurosci.",
  volume=  13,
  pages= 1159,
  year= 2019
}