NM000109: eeg dataset, 36 subjects#

EEG During Mental Arithmetic Tasks

Access recordings and metadata through EEGDash.

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

Modality: eeg Subjects: 36 Recordings: 72 License: ODC-By-1.0 Source: nemar

Metadata: Complete (100%)

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#

DOI

EEG During Mental Arithmetic Tasks

Introduction

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.

Overview of the experiment

View full README

DOI

EEG During Mental Arithmetic Tasks

Introduction

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.

Overview of the experiment

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).

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).

Dataset Information#

Dataset ID

NM000109

Title

EEG During Mental Arithmetic Tasks

Author (year)

Zyma2019

Canonical

—

Importable as

NM000109, Zyma2019

Year

2000

Authors

Igor Zyma, Sergii Tukaev, Ivan Seleznov, Ken Kiyono, Anton Popov, Mariia Chernykh, Oleksii Shpenkov

License

ODC-By-1.0

Citation / DOI

10.82901/nemar.nm000109

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},
}

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

  • Subjects: 36

  • Recordings: 72

  • Tasks: 2

Channels & sampling rate

  • Channels: 21

  • Sampling rate (Hz): 500

  • Duration (hours): 2.40996

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 174.5 MB

  • File count: 72

  • Format: BIDS

License & citation

  • License: ODC-By-1.0

  • DOI: 10.82901/nemar.nm000109

Provenance

API Reference#

Use the NM000109 class to access this dataset programmatically.

class eegdash.dataset.NM000109(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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

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/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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#