EEGdash›OpenNeuro›DS007788

Iss. 7788 · 7 subjects · 935 recordings · CC0

Dataset Brief · Dataset

DS007788: eeg dataset, 7 subjects#

Dataset: EEG-Controlled Exoskeleton for Walking and Standing - A Longitudinal Study of Healthy Individuals

Citation: Shantanu Sarkar, Kevin Nathan, Jose L. Contreras-Vidal (—). Dataset: EEG-Controlled Exoskeleton for Walking and Standing - A Longitudinal Study of Healthy Individuals. 10.18112/openneuro.ds007788.v1.0.1

7-participant EEG dataset — Dataset: EEG-Controlled Exoskeleton for Walking and Standing - A Longitudinal Study of Healthy Individuals.

Data & curation Shantanu Sarkar · Kevin Nathan · Jose L. Contreras-Vidal
Funding UH–Methodist Graduate Fellowship for Translational Research · NSF IUCRC BRAIN Center Award #2137255

EEG · 64 ch100 HzBIDS 1.8.015 tasks9 sessions

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007788

dataset = DS007788(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = DS007788(cache_dir="./data", subject="01")

Advanced query

dataset = DS007788(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds007788,
  title = {Dataset: EEG-Controlled Exoskeleton for Walking and Standing - A Longitudinal Study of Healthy Individuals},
  author = {Shantanu Sarkar and Kevin Nathan and Jose L. Contreras-Vidal},
  doi = {10.18112/openneuro.ds007788.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds007788.v1.0.1},
}

§ 02Study · The README

About This Dataset#

This dataset comprises multimodal recordings from a brain–machine interface (BMI) training study involving seven healthy adult participants (ages 20–30 years; mean = 24.3, SD = 3.8). The study investigated both open-loop and closed-loop control of a lower-limb exoskeleton (Rex Bionics). The dataset includes 60-channel EEG, 4-channel EOG, motion data from two IMU sensors (mounted on the participant’s forehead and the exoskeleton), and exoskeleton control and feedback signals. Each participant completed nine sessions over several weeks, with each session structured into a training phase followed by a closed-loop trial phase.

Participants perform motor imagery tasks without feedback to calibrate the BMI decoder.

TrialXX: Each session includes 12 closed-loop BMI trials (trial01–trial12), during which participants use motor imagery to control the exoskeleton in real time. - Block 1: Trials 1–4 - Block 2: Trials 5–8 - Block 3: Trials 9–12

EEG-Controlled Exoskeleton for Walking and Standing – A Longitudinal Motor Imagery Study in Healthy Adults

Dataset Overview

walk6min / stop6min: After the 12 trials, participants complete two extended motor imagery tasks: - walk6min: Imagining continuous walking for 6 minutes - stop6min: Imagining standing still for 6 minutes
View full README
EEG-Controlled Exoskeleton for Walking and Standing – A Longitudinal Motor Imagery Study in Healthy Adults

Dataset Overview

walk6min / stop6min: After the 12 trials, participants complete two extended motor imagery tasks: - walk6min: Imagining continuous walking for 6 minutes - stop6min: Imagining standing still for 6 minutes

Data Modalities

EEG: 60 scalp channels + 4 EOG channels

IMU: 3-axis accelerometer, gyroscope, magnetometer, and quaternion

Sensor Placement: IMUs mounted on the participant’s forehead and the exosuit back brace

Decoder Signals / Feedback: Logged control signals and BMI predictions

Additional Materials

MIQ‑RS: Motor Imagery Questionnaire – Revised Second Version (PDFs in derivatives/MIQ-RS/)

Validation Tables: Data availability, synchronization, and electrode placement (derivatives/validation/)

Raw Data: Provided without filtering or artifact removal

Folder Structure
dataset-root/
│
├── sub-{SubNo}/
│   └── ses-{SesNo}/
│       ├── eeg/
│       │   ├── sub-{SubNo}_ses-{SesNo}_coordsystem.json
│       │   ├── sub-{SubNo}_ses-{SesNo}_electrodes.tsv
│       │   ├── sub-{SubNo}_ses-{SesNo}_electrodes.json
│       │   │
│       │   ├── sub-{SubNo}_ses-{SesNo}_task-{Task}_eeg.edf
│       │   ├── sub-{SubNo}_ses-{SesNo}_task-{Task}_eeg.json
│       │   │
│       │   ├── sub-{SubNo}_ses-{SesNo}_task-{Task}_acq-{AcqLabel}_events.tsv
│       │   ├── sub-{SubNo}_ses-{SesNo}_task-{Task}_acq-{AcqLabel}_events.json
│       │   │
│       │   ├── sub-{SubNo}_ses-{SesNo}_task-{Task}_recording-{StimLabel}_stim.tsv.gz
│       │   └── sub-{SubNo}_ses-{SesNo}_task-{Task}_recording-{StimLabel}_stim.json
│       │
│       └── motion/
│           ├── sub-{SubNo}_ses-{SesNo}_task-{Task}_tracksys-{IMUPos}_motion.tsv
│           ├── sub-{SubNo}_ses-{SesNo}_task-{Task}_tracksys-{IMUPos}_motion.json
│           ├── sub-{SubNo}_ses-{SesNo}_task-{Task}_tracksys-{IMUPos}_channels.tsv
│           └── sub-{SubNo}_ses-{SesNo}_task-{Task}_tracksys-{IMUPos}_channels.json
│
└── derivatives/
    ├── MIQ-RS/
    │   └── sub-{SubNo}_MIQ-RS.pdf
    │
    └── validation/
        ├── BeepValidation.xls
        ├── ExoIMU_Stats.xls
        ├── ExoIMU_W_vs_S.xls
        ├── HeadIMU_Stats.xls
        ├── HeadIMU_W_vs_S.xls
        ├── IMU_Orientation.xls
        ├── Training-OpenLoop-Stats.xls
        ├── Trial-CloseLoop-Stats.xls
        ├── failCounterValidation.xls
        ├── infoClosedLoopValidation.xls
        ├── rexCommandValidation.xls
        └── rexStateValidation.xls
Naming Convention
| Placeholder | Description | Values |
|---|---|---|
| `{SubNo}` | Participant ID | 01–07 |
| `{SesNo}` | Session Number | 01–09 |
| `{Task}` | Task name | `training`, `trial01–trial12`, `stop6min`, `walk6min` |
| `{AcqLabel}` | Acquisition label | `infoclosedloop`, `rexcommand`, `rexstate` |
| `{StimLabel}` | Stimulus label | `beep`, `failcounter` |
| `{IMUPos}` | IMU position | `head`, `exo` |
Citation

If you use this dataset, please cite the associated study and acknowledge the contributors.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 64 ch (n=935 recordings)

Sampling frequencies: 100.0 Hz (n=935 recordings)

Total recording duration: 34 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 64 ch · EEG · 100 Hz · 7 subjects, 935 recordings

Live trace viewer — sub-07 · ses-08 · task-stop6min

Showing one representative recording out of 7 subjects and 935 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 60 sensors — 60 channels

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

HED event descriptors word cloud

§ 05Manifest · BIDS tree

Manifest#

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS007788(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asDS007788

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.DS007788(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Dataset: EEG-Controlled Exoskeleton for Walking and Standing - A Longitudinal Study of Healthy Individuals

Study:: ds007788 (OpenNeuro)
Author (year):: nan
Canonical:: —

Also importable as: DS007788, nan.

Modality: eeg. Subjects: 7; recordings: 935; tasks: 15.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007788 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007788 DOI: https://doi.org/10.18112/openneuro.ds007788.v1.0.1

Examples

>>> from eegdash.dataset import DS007788
>>> dataset = DS007788(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

save(path: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)

Parameters:

path (str) –

Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS007788.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for ds007788 to reproduce the tutorial on this dataset.

Citation

Shantanu Sarkar, Kevin Nathan, Jose L. Contreras-Vidal (n.d.). Dataset: EEG-Controlled Exoskeleton for Walking and Standing - A Longitudinal Study of Healthy Individuals. 10.18112/openneuro.ds007788.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds007788.v1.0.1.

Related & sibling datasets

DS007541EEG · 32 subj DS007720EEG · 5 subj DS004350EEG · 24 subj DS005262EEG · 12 subj DS004408EEG · 19 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.8.0

Sidecars

electrodes · coordsystem · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds007788.v1.0.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS007788`
Title	Dataset: EEG-Controlled Exoskeleton for Walking and Standing - A Longitudinal Study of Healthy Individuals
Author (year)	—
Canonical	—
Importable as	`DS007788`
Year	—
Authors	Shantanu Sarkar, Kevin Nathan, Jose L. Contreras-Vidal
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007788.v1.0.1
Source links	OpenNeuro \| NeMAR \| Source URL

DS007788: eeg dataset, 7 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#