NM000114: eeg dataset, 64 subjects#
MDD Patients and Healthy Controls EEG Data
Citation: Wajid Mumtaz, Likun Xia, Syed Saad Azhar Ali, Mohd Azhar Mohd Yasin, Mazhar Hussain, Aamir Saeed Malik (2017). MDD Patients and Healthy Controls EEG Data. 10.82901/nemar.nm000114
64-participant EEG dataset — MDD Patients and Healthy Controls EEG Data.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import NM000114
dataset = NM000114(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = NM000114(cache_dir="./data", subject="01")
Advanced query
dataset = NM000114(
cache_dir="./data",
query={"subject": {"$in": ["01", "02"]}},
)
Iterate recordings
for rec in dataset:
print(rec.subject, rec.raw.info['sfreq'])
If you use this dataset in your research, please cite the original authors.
BibTeX
@dataset{nm000114,
title = {MDD Patients and Healthy Controls EEG Data},
author = {Wajid Mumtaz and Likun Xia and Syed Saad Azhar Ali and Mohd Azhar Mohd Yasin and Mazhar Hussain and Aamir Saeed Malik},
doi = {10.82901/nemar.nm000114},
url = {https://doi.org/10.82901/nemar.nm000114},
}
About This Dataset#
This dataset contains resting-state and task-based EEG recordings from patients diagnosed with Major Depressive Disorder (MDD) and healthy control participants (H). The data was collected to investigate differences in brain electrical activity between MDD patients and healthy individuals across different mental states. The dataset includes 34 participants (19 healthy controls and 15 MDD patients) with recordings during eyes-closed rest, eyes-open rest, and an auditory oddball P300 task. This dataset enables research on neurophysiological biomarkers of depression, comparative studies of brain activity patterns between clinical and healthy populations, and investigation of attentional processing differences in MDD.
Participants underwent three recording conditions: (1) eyes-closed resting state, (2) eyes-open resting state, and (3) an auditory oddball P300 task. During the resting-state conditions, participants were instructed to sit quietly with either their eyes closed (EC) or eyes open (EO) for the duration of the recording. In the P300 task, participants were presented with auditory stimuli consisting of frequent standard tones (80% probability) and infrequent target tones (20% probability), and were required to mentally count the target tones. EEG was recorded using a 19-channel monopolar EEG system with electrodes positioned according to the International 10-20 system, referenced to linked ears (A1+A2). The sampling rate was 256 Hz. Hardware filters included a high-pass filter at 0.5 Hz and a low-pass filter at 70 Hz, with a 50 Hz notch filter to remove power line noise. All electrode impedances were maintained below 5 kΩ. The recordings were conducted in a controlled environment to minimize external artifacts. One participant (MDD S15) had two separate recording sessions, resulting in duplicate recordings for this subject.
MDD Patients and Healthy Controls EEG Data
Introduction
Description of the preprocessing if any
The original EDF files have been converted to BIDS format. Channel names have been standardized by extracting the electrode names from the original “EEG <electrode>-<reference>” format. Channels originally referenced to the left ear (LE) are now labeled with just the electrode name, while other bipolar derivations (e.g., A2-A1, 23A-23R, 24A-24R) retain their bipolar notation in the format “<electrode1>-<electrode2>”. The dataset includes 19 standard EEG channels plus three additional bipolar channels. Subject IDs have been prefixed with their diagnostic group (“H” for healthy controls, “MDD” for Major Depressive Disorder patients) to facilitate group comparisons. All recordings were artifact-free segments selected from longer recording sessions, with epochs containing excessive oculographic or myographic artifacts excluded during initial data collection.
View full README
MDD Patients and Healthy Controls EEG Data
Introduction
Description of the preprocessing if any
The original EDF files have been converted to BIDS format. Channel names have been standardized by extracting the electrode names from the original “EEG <electrode>-<reference>” format. Channels originally referenced to the left ear (LE) are now labeled with just the electrode name, while other bipolar derivations (e.g., A2-A1, 23A-23R, 24A-24R) retain their bipolar notation in the format “<electrode1>-<electrode2>”. The dataset includes 19 standard EEG channels plus three additional bipolar channels. Subject IDs have been prefixed with their diagnostic group (“H” for healthy controls, “MDD” for Major Depressive Disorder patients) to facilitate group comparisons. All recordings were artifact-free segments selected from longer recording sessions, with epochs containing excessive oculographic or myographic artifacts excluded during initial data collection.
Description of the event values if any
No events.tsv files are provided as the recordings represent continuous resting-state or task conditions without discrete trial markers. The experimental condition for each recording is indicated by the “task” field in the BIDS filename: - “eyesClosed”: eyes-closed resting state - “eyesOpen”: eyes-open resting state - “P300”: auditory oddball task (continuous recording during the entire task block)
For the P300 task recordings, while individual stimulus onsets are not marked in events.tsv files, the entire recording represents the period during which participants performed the auditory oddball counting task.
Citation
When using this dataset, please cite: 1. Mumtaz, W., Xia, L., Ali, S. S. A., Yasin, M. A. M., Hussain, M., & Malik, A. S. (2017). Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD). Biomedical Signal Processing and Control, 31, 108-115. https://doi.org/10.1016/j.bspc.2016.07.006 2. Mumtaz, Wajid (2016). MDD Patients and Healthy Controls EEG Data (New). figshare. Dataset. https://doi.org/10.6084/m9.figshare.4244171.v2
Data curators: Pierre Guetschel (BIDS conversion) Original data collection team: - Wajid Mumtaz (Universiti Teknologi PETRONAS) - Likun Xia (Universiti Teknologi PETRONAS) - Syed Saad Azhar Ali (Universiti Teknologi PETRONAS) - Mohd Azhar Mohd Yasin (Universiti Teknologi PETRONAS) - Mazhar Hussain (Universiti Teknologi PETRONAS)
- Aamir Saeed Malik (Universiti Teknologi PETRONAS)
Automatic report
Report automatically generated by ``mne_bids.make_report()``.
The MDD Patients and Healthy Controls EEG Data dataset was created by Wajid
Mumtaz, Likun Xia, Syed Saad Azhar Ali, Mohd Azhar Mohd Yasin, Mazhar Hussain, and Aamir Saeed Malik and conforms to BIDS version 1.7.0. This report was generated with MNE-BIDS (https://doi.org/10.21105/joss.01896). The dataset consists of 64 participants (sex were all unknown; handedness were all unknown; ages all unknown) . Data was recorded using an EEG system sampled at 256.0 Hz with line noise at n/a Hz. There were 181 scans in total. Recording durations ranged from 180.0 to 686.0 seconds (mean = 408.84, std = 155.23), for a total of 74000.29 seconds of data recorded over all scans. For each dataset, there were on average 21.24 (std = 0.97) recording channels per scan, out of which 21.24 (std = 0.97) were used in analysis (0.0 +/- 0.0 were removed from analysis).
Cohort#
Dataset Statistics#
Channel counts (ch)
Sampling frequencies: 256.0 Hz (n=181 recordings)
Total recording duration: 20 h 33 min
Signal · Electrodes & live trace#
Live trace viewer — sub-HS21 · task-eyesOpen
Showing one representative recording out of
64 subjects and 181 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 · 19 sensors — 19 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
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.
Full dataset metadata table
Dataset ID |
|
Title |
MDD Patients and Healthy Controls EEG Data |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2017 |
Authors |
Wajid Mumtaz, Likun Xia, Syed Saad Azhar Ali, Mohd Azhar Mohd Yasin, Mazhar Hussain, Aamir Saeed Malik |
License |
CC-BY-4.0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{nm000114,
title = {MDD Patients and Healthy Controls EEG Data},
author = {Wajid Mumtaz and Likun Xia and Syed Saad Azhar Ali and Mohd Azhar Mohd Yasin and Mazhar Hussain and Aamir Saeed Malik},
doi = {10.82901/nemar.nm000114},
url = {https://doi.org/10.82901/nemar.nm000114},
}
API Reference#
eegdash.datasetEEGDashDatasetNM000114 · Mumtaz2017eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.NM000114(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
MDD Patients and Healthy Controls EEG Data
- Study:
nm000114(NeMAR)- Author (year):
Mumtaz2017- Canonical:
—
Also importable as:
NM000114,Mumtaz2017.Modality:
eeg. Subjects: 64; recordings: 181; tasks: 3.- 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/nm000114 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000114 DOI: https://doi.org/10.82901/nemar.nm000114
Examples
>>> from eegdash.dataset import NM000114 >>> dataset = NM000114(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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorch
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 nm000114 to reproduce the tutorial on this dataset.
Citation
Wajid Mumtaz, Likun Xia, Syed Saad Azhar Ali, Mohd Azhar Mohd Yasin, Mazhar Hussain, … (2017). MDD Patients and Healthy Controls EEG Data. 10.82901/nemar.nm000114
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.nm000114.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset