To leverage recent and ongoing advancements in large-scale computational methods and to ensure the preservation of scientific data generated from publicly funded research, the EEG-DaSh data archive will create a data-sharing resource for MEEG (EEG, MEG) data contributed by collaborators for machine learning (ML) and deep learning (DL) applications.
The data in EEG-DaSh originates from a collaboration involving 25 laboratories, encompassing 27,053 participants. This extensive collection includes MEEG data, which is a combination of EEG and MEG signals. The data is sourced from various studies conducted by these labs, involving both healthy subjects and clinical populations with conditions such as ADHD, depression, schizophrenia, dementia, autism, and psychosis. Additionally, data spans different mental states like sleep, meditation, and cognitive tasks. In addition, EEG-DaSh will incorporate a subset of the data converted from NEMAR, which includes 330 MEEG BIDS-formatted datasets, further expanding the archive with well-curated, standardized neuroelectromagnetic data.
The following HBN datasets are currently featured on EEGDash. Documentation about these datasets is available here.
| DatasetID | Participants | Files | Sessions | Population | Channels | Is 10-20? | Modality | Size |
|---|---|---|---|---|---|---|---|---|
| ds005505 | 136 | 5393 | 1 | Healthy | 129 | other | Visual | 103 GB |
| ds005506 | 150 | 5645 | 1 | Healthy | 129 | other | Visual | 112 GB |
| ds005507 | 184 | 7273 | 1 | Healthy | 129 | other | Visual | 140 GB |
| ds005508 | 324 | 13393 | 1 | Healthy | 129 | other | Visual | 230 GB |
| ds005510 | 135 | 4933 | 1 | Healthy | 129 | other | Visual | 91 GB |
| ds005512 | 257 | 9305 | 1 | Healthy | 129 | other | Visual | 157 GB |
| ds005514 | 295 | 11565 | 1 | Healthy | 129 | other | Visual | 185 GB |
A total of 246 other datasets are also available through EEGDash.
EEGDash queries return a Pytorch Dataset formatted to facilitate machine learning (ML) and deep learning (DL) applications. PyTorch Datasets are the best format for EEGDash queries because they provide an efficient, scalable, and flexible structure for machine learning (ML) and deep learning (DL) applications. They allow seamless integration with PyTorch’s DataLoader, enabling efficient batching, shuffling, and parallel data loading, which is essential for training deep learning models on large EEG datasets.
EEGDash datasets are processed using the popular BrainDecode library. In fact, EEGDash datasets are BrainDecode datasets, which are themselves PyTorch datasets. This means that any preprocessing possible on BrainDecode datasets is also possible on EEGDash datasets. Refer to BrainDecode tutorials for guidance on preprocessing EEG data.
Use your preferred Python environment manager with Python > 3.9 to install the package.
- To install the eegdash package, use the following command:
pip install eegdash - To verify the installation, start a Python session and type:
from eegdash import EEGDash
To use the data from a single subject, enter:
from eegdash import EEGDashDataset
ds_NDARDB033FW5 = EEGDashDataset({'dataset': 'ds005514', 'task': 'RestingState', 'subject': 'NDARDB033FW5'})This will search and download the metadata for the task RestingState for subject NDARDB033FW5 in BIDS dataset ds005514. The actual data will not be downloaded at this stage. Following standard practice, data is only downloaded once it is processed. The ds_NDARDB033FW5 object is a fully functional BrainDecode dataset, which is itself a PyTorch dataset. This tutorial shows how to preprocess the EEG data, extracting portions of the data containing eyes-open and eyes-closed segments, then perform eyes-open vs. eyes-closed classification using a (shallow) deep-learning model.
To use the data from multiple subjects, enter:
from eegdash import EEGDashDataset
ds_ds005505rest = EEGDashDataset({'dataset': 'ds005505', 'task': 'RestingState'}, target_name='sex')This will search and download the metadata for the task 'RestingState' for all subjects in BIDS dataset 'ds005505' (a total of 136). As above, the actual data will not be downloaded at this stage so this command is quick to execute. Also, the target class for each subject is assigned using the target_name parameter. This means that this object is ready to be directly fed to a deep learning model, although the tutorial script performs minimal processing on it, prior to training a deep-learning model. Because 14 gigabytes of data are downloaded, this tutorial takes about 10 minutes to execute.
EEGDash automatically caches the downloaded data in the .eegdash_cache folder of the current directory from which the script is called. This means that if you run the tutorial scripts, the data will only be downloaded the first time the script is executed.
We organize workshops and educational events to foster cross-cultural education and student training, offering both online and in-person opportunities in collaboration with US and Israeli partners. Events for 2025 will be announced via the EEGLABNEWS mailing list. Be sure to subscribe.
EEG-DaSh is a collaborative initiative between the United States and Israel, supported by the National Science Foundation (NSF). The partnership brings together experts from the Swartz Center for Computational Neuroscience (SCCN) at the University of California San Diego (UCSD) and Ben-Gurion University (BGU) in Israel.
