NM000109: eeg dataset, 36 subjects#
EEG During Mental Arithmetic Tasks
Citation: Igor Zyma, Sergii Tukaev, Ivan Seleznov, Ken Kiyono, Anton Popov, Mariia Chernykh, Oleksii Shpenkov (2000). EEG During Mental Arithmetic Tasks. 10.82901/nemar.nm000109
36-participant EEG dataset — EEG During Mental Arithmetic Tasks.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import NM000109
dataset = NM000109(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = NM000109(cache_dir="./data", subject="01")
Advanced query
dataset = NM000109(
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{nm000109,
title = {EEG During Mental Arithmetic Tasks},
author = {Igor Zyma and Sergii Tukaev and Ivan Seleznov and Ken Kiyono and Anton Popov and Mariia Chernykh and Oleksii Shpenkov},
doi = {10.82901/nemar.nm000109},
url = {https://doi.org/10.82901/nemar.nm000109},
}
About This Dataset#
This dataset contains scalp EEG recordings from 36 healthy university students (9 male, 27 female; ages 18-26 years) during mental arithmetic tasks and resting-state periods. The study was designed to investigate EEG correlates of cognitive activity during intensive mental workload involving serial subtraction. The dataset provides brain electrical activity measurements for studying the neural mechanisms of mathematical cognition and cognitive stress responses, with potential applications in cognitive neuroscience research, mental workload assessment, and brain-computer interface development.
Participants were recorded during two conditions: (1) resting-state with eyes closed, and (2) mental arithmetic task involving serial subtraction. During the resting state, participants sat comfortably in a dark, soundproof chamber and were instructed to relax. After a 3-minute adaptation period, a 3-minute resting-state EEG recording was made with eyes closed. Participants then performed a 4-minute mental arithmetic task during which they were presented with a 4-digit minuend and 2-digit subtrahend (e.g., 3141 - 42) and performed serial subtractions mentally. They were instructed to count accurately and quickly in their self-determined rhythm without speaking or using finger movements. The dataset stores the last 3 minutes of the rest period (180 seconds) and the first minute of mental arithmetic performance (60 seconds) for each participant. EEG was recorded using a Neurocom 23-channel monopolar system sampled at 500 Hz with electrodes placed according to the International 10/20 system and referenced to interconnected ear electrodes. Filters included a high-pass filter (0.5 Hz cut-off), low-pass filter (45 Hz cut-off), and power line notch filter (50 Hz). Participants were divided post-hoc into two performance groups based on the number of completed arithmetic operations: “good counters” (Group G, n=24, mean operations=21, SD=7.4) and “bad counters” (Group B, n=12, mean operations=7, SD=3.6).
EEG During Mental Arithmetic Tasks
Introduction
Description of the preprocessing if any
All recordings included only artifact-free EEG segments, with 30 of 66 initially recorded participants excluded due to excessive oculographic and myographic artifacts. Channel names have been standardized to match the International 10-20 nomenclature. The raw EDF files have been converted to BIDS format with proper channel type assignments (EEG for brain signals). Subject birth years were calculated from age and recording year. Recording dates have been set to January 1st of the recording year due to privacy considerations in the original dataset. Impedance checks confirmed all electrodes were below 5 kΩ prior to recording.
View full README
EEG During Mental Arithmetic Tasks
Introduction
Description of the preprocessing if any
All recordings included only artifact-free EEG segments, with 30 of 66 initially recorded participants excluded due to excessive oculographic and myographic artifacts. Channel names have been standardized to match the International 10-20 nomenclature. The raw EDF files have been converted to BIDS format with proper channel type assignments (EEG for brain signals). Subject birth years were calculated from age and recording year. Recording dates have been set to January 1st of the recording year due to privacy considerations in the original dataset. Impedance checks confirmed all electrodes were below 5 kΩ prior to recording.
Description of the event values if any
No events.tsv files are provided. The “task” field in the BIDS filenames indicates the experimental condition: - “rest”: resting-state condition - “mentalArithmetic”: mental arithmetic task condition
Citation
When using this dataset, please cite: 1. Zyma I, Tukaev S, Seleznov I, Kiyono K, Popov A, Chernykh M, Shpenkov O. Electroencephalograms during Mental Arithmetic Task Performance. Data. 2019; 4(1):14. https://doi.org/10.3390/data4010014 2. PhysioNet database: https://doi.org/10.13026/C2JQ1P 3. Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., … & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
Data curators: Pierre Guetschel (BIDS conversion) Original data collection team: - Igor Zyma, PhD (National Technical University of Ukraine) - Sergii Tukaev (National Technical University of Ukraine) - Ivan Seleznov (National Technical University of Ukraine) - Ken Kiyono, PhD - Anton Popov (National Technical University of Ukraine) - Mariia Chernykh (National Technical University of Ukraine)
- Oleksii Shpenkov (National Technical University of Ukraine)
Automatic report
Report automatically generated by ``mne_bids.make_report()``.
The EEG During Mental Arithmetic Tasks dataset was created by Igor Zyma, Sergii
Tukaev, Ivan Seleznov, Ken Kiyono, Anton Popov, Mariia Chernykh, and Oleksii Shpenkov 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 36 participants (comprised of 9 male and 27 female participants; handedness were all unknown; ages ranged from 16.0 to 26.0 (mean = 18.25, std = 2.14)) . Data was recorded using an EEG system sampled at 500.0 Hz with line noise at n/a Hz.
There were 72 scans in total. Recording durations ranged from 62.0 to 188.0 seconds (mean = 120.5, std = 59.71), for a total of 8675.86 seconds of data recorded over all scans. For each dataset, there were on average 21.0 (std = 0.0) recording channels per scan, out of which 21.0 (std = 0.0) were used in analysis (0.0 +/- 0.0 were removed from analysis).
Cohort#
Dataset Statistics#
Age distribution by gender (n=36, range 16–26 yr, mean 18.2 yr)
Sex composition
Channel counts: 21 ch (n=72 recordings)
Sampling frequencies: 500.0 Hz (n=72 recordings)
Total recording duration: 2 h 24 min
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-rest
Showing one representative recording out of
36 subjects and 72 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 · 20 sensors — 20 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 |
EEG During Mental Arithmetic Tasks |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2000 |
Authors |
Igor Zyma, Sergii Tukaev, Ivan Seleznov, Ken Kiyono, Anton Popov, Mariia Chernykh, Oleksii Shpenkov |
License |
ODC-By-1.0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{nm000109,
title = {EEG During Mental Arithmetic Tasks},
author = {Igor Zyma and Sergii Tukaev and Ivan Seleznov and Ken Kiyono and Anton Popov and Mariia Chernykh and Oleksii Shpenkov},
doi = {10.82901/nemar.nm000109},
url = {https://doi.org/10.82901/nemar.nm000109},
}
API Reference#
eegdash.datasetEEGDashDatasetNM000109 · Zyma2019eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.NM000109(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
EEG During Mental Arithmetic Tasks
- Study:
nm000109(NeMAR)- Author (year):
Zyma2019- Canonical:
—
Also importable as:
NM000109,Zyma2019.Modality:
eeg. Subjects: 36; recordings: 72; tasks: 2.- 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/nm000109 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000109 DOI: https://doi.org/10.82901/nemar.nm000109
Examples
>>> from eegdash.dataset import NM000109 >>> dataset = NM000109(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 nm000109 to reproduce the tutorial on this dataset.
Citation
Igor Zyma, Sergii Tukaev, Ivan Seleznov, Ken Kiyono, Anton Popov, … (2000). EEG During Mental Arithmetic Tasks. 10.82901/nemar.nm000109
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.nm000109.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset