EEG During Mental Arithmetic Tasks

Creators

• Igor Zyma
• Ivan Seleznov
• Anton Popov
• Mariia Chernykh
• Oleksii Shpenkov

Publication

Published: Dec. 17, 2018. Version: 1.0.0

Citations

Please cite the original publication:

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

Also include the standard PhysioNet citation:

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.

Introduction

The database contains EEG recordings of subjects before and during the performance of mental arithmetic tasks.

Study Methods

• EEGs were recorded monopolarly using the Neurocom EEG 23-channel system (Ukraine, XAI-MEDICA).
• Electrodes were placed according to the International 10/20 scheme, referenced to interconnected ear electrodes.
• High-pass filter: 30 Hz cut-off. Power line notch filter: 50 Hz.
• Recordings are 60 seconds long, artifact-free segments. Artifacts were removed using Independent Component Analysis (ICA).
• The mental task was serial subtraction of two orally communicated numbers (4-digit minus 2-digit).
• Participants had normal cognitive/visual status; exclusion criteria included psychiatric/neurological issues and substance abuse.

Data Description

• Data provided in EDF format.
• Each subject has two files:
  • _1: Background EEG (before task).
  • _2: EEG during mental arithmetic task.
• Datetime set to Jan 01 for all files.
• Subjects divided into two groups:
  • Group "G" (24 subjects): Good quality counting (Mean: 21 operations/4 min).
  • Group "B" (12 subjects): Poor quality counting (Mean: 7 operations/4 min).
• subject-info.csv contains:
  • Gender
  • Age
  • Occupation
  • Date of recording
  • Count quality (0: Poor, 1: Good)

File Access and Viewing

EDF files can be viewed using:

• Polyman (Windows only)
• EDFbrowser (Linux, macOS, Windows) — www.teuniz.net
• LightWAVE and PhysioBank ATM (web-based, platform-independent)
• WAVE and other WFDB Software tools

For Python users, recommended library: PyEDFlib.

Contributors

National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Department of Electronic Engineering:

• Igor Zyma
• Sergii Tukaev
• Ivan Seleznov

Contact

For more information:

Ivan Seleznov
National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Department of Electronic Engineering.
Email: ivan.seleznov1@gmail.com