Arnaud Delorme
University of California-San Diego, CA·Neuroscience
The brain's electrical activity can be recorded with electrodes placed on the scalp, a cheap and widely used method called electroencephalography (EEG). Thousands of EEG studies have been run on learning, attention, decision making and neurological disease, but the recordings sit in many separate online archives that do not speak the same language, and most are hosted outside the United States.
- Fields
Neuroscience
Computer Science
Psychology
- Topics
electroencephalography
magnetoencephalography
Brain Imaging Data Structure
- Grants
- 2 known grants, through Aug 2031
What they work on
- Consolidate human EEG datasets from major international repositories into a single U.S.-hosted resource, linking each to its original source and pairing recordings with behavioral measures, stimulus events, response times and eye tracking.
- Annotate datasets with Hierarchical Event Descriptors, a controlled vocabulary for experimental events, so studies can be searched by experimental design, stimulus type or cognitive process rather than by file name.
- Build automated quality-assessment pipelines and programming bridges connecting the archive to EEGLAB, MNE-Python, FieldTrip and Braindecode, so stored data feed directly into standard and deep-learning analyses.
- Create the EEG-DaSh web platform for sharing EEG and MEG recordings from over 25 partner labs in a storage format optimized for machine learning and deep learning.
- Develop high-performance-computing pipelines for EEG-DaSh that extract features, measure critical brain dynamics and inter-region connectivity, and use neural field and neural mass models to generate simulated data for augmentation.
- Sustain the infrastructure through a distributed steering committee from major tool ecosystems, training hackathons that develop long-term maintainers, open analysis competitions, and institutional hosting partnerships.
More about the research
Researchers therefore cannot easily search across studies or pool them, which blocks the kind of large-scale analyses and machine learning that need lots of data. Both projects here build shared infrastructure that gathers EEG recordings, labels them consistently, and connects them to the software people already use.
Why it matters
Publicly funded EEG recordings are often collected once and never reused. Pooling and consistently labeling them lets scientists ask questions no single study can answer about how brain activity relates to behavior, and gives machine-learning researchers the volume of data needed to train models that may reveal patterns relevant to cognition and neurological disorders. Open computing resources and training also lower the cost of entry for smaller labs.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Richard M Leahy
University of Southern California, CA·Neuroscience
Develop Brainstorm tools for experiments that use naturalistic, non-repeating stimuli, including machine- and human-readable labels for uncontrolled events and behavior, so these can be linked to recorded brain activity.
Daniel Andresen
Kansas State University, KS·Computer Science
Provide secure, fast data sharing, analytics, computational modeling and high-performance computing for neuroscience datasets such as fMRI, diffusion tensor imaging, EEG and electrophysiology recordings.
Alexandra Bey
Duke University, NC·Neuroscience·Early-career
Confirm that less looking at social than nonsocial images goes with lower beta-1 and higher theta EEG brain rhythms in autistic preschoolers, using EEG recorded at the same time as automated eye-gaze tracking.
The school
University of California-San Diego: 84 labs on PI Pages, $1.9B a year on research, 637 research doctorates a year.
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