Version 1.0 - 18/12/2018
When referencing this material, please cite:
@article{EEGMAT,
author = {Zyma, Igor and Tukaev, Sergii and Seleznov, Ivan and Kiyono, Ken
and Popov, Anton and Chernykh, Mariia and Shpenkov, Oleksii},
title = {Electroencephalograms during Mental Arithmetic Task Performance},
journal = {Data},
volume = {4},
year = {2019},
number = {1},
article-number = {14},
issn = {2306-5729},
doi = {10.3390/data4010014},
url = {http://www.mdpi.com/2306-5729/4/1/14}
}
Please also include the standard citation for PhysioNet:
@article{PhysioNet,
author = {Goldberger, Ary L. and Amaral, Luis A. N.
and Glass, Leon and Hausdorff, Jeffrey M.
and Ivanov, Plamen Ch. and Mark, Roger G.
and Mietus, Joseph E. and Moody, George B.
and Peng, Chung-Kang and Stanley, H. Eugene},
title = {{PhysioBank}, {PhysioToolkit}, and {PhysioNet}:
Components of a New Research Resource for Complex
Physiologic Signals},
journal = {Circulation},
publisher = {American Heart Association, Inc.},
volume = {101},
number = {23},
year = {2000},
month = {June},
pages = {e215--e220},
doi = {10.1161/01.CIR.101.23.e215},
issn = {0009-7322},
url = {http://circ.ahajournals.org/content/101/23/e215}
}
Introduction
The database contains EEG recordings of subjects before and during the performance of mental arithmetic tasks.
Study Methods
The EEGs were recorded monopolarly using Neurocom EEG 23-channel system (Ukraine, XAI-MEDICA). The silver/silver chloride electrodes were placed on the scalp according to the International 10/20 scheme. All electrodes were referenced to the interconnected ear reference electrodes.
A high-pass filter with a 30 Hz cut-off frequency and a power line notch filter (50 Hz) were used. All recordings are artifact-free EEG segments of 60 seconds duration. At the stage of data preprocessing, the Independent Component Analysis (ICA) was used to eliminate the artifacts (eyes, muscle, and cardiac overlapping of the cardiac pulsation). The arithmetic task was the serial subtraction of two numbers. Each trial started with the communication orally 4-digit (minuend) and 2-digit (subtrahend) numbers (e.g. 3141 and 42).
The participants were eligible to enroll in the study if they had normal or corrected-to-normal visual acuity, normal color vision, had no clinical manifestations of mental or cognitive impairment, verbal or non-verbal learning disabilities. Exclusion criteria were the use of psychoactive medication, drug or alcohol addiction and psychiatric or neurological complaints.
Data
The data files with EEG are provided in EDF (European Data Format) format. Each subject has 2 files:
- with "_1" suffix -- the recording of the background EEG of a subject (before mental arithmetic task)
- with "_2" suffix -- the recording of EEG during the mental arithmetic task.
The recording datetime information has been set to Jan 01 for all files.
In this experiment all subjects are divided into two groups:
- Group "G" (24 subjects) performing good quality count (Mean number of operations per 4 minutes = 21, SD = 7.4).
- Group "B" (12 subjects) performing bad quality count (Mean number of operations per 4 minutes = 7, SD = 3.6).
In subject-info.csv, the "Count quality" column indicates which subjects correspond to which group (0 - Group "B", 1 - Group "G"). Additionally, subject-info.csv provides basic information about each subject (gender, age, job, date of recording).
Both EDF and EDF+ formats are free and can be viewed using free software such as:
- Polyman (for MS-Windows only; for details, please follow the link) EDFbrowser (for Linux, Mac OS X, and MS-Windows; at www.teuniz.net)
- LightWAVE and the PhysioBank ATM, platform-independent web applications from PhysioNet
- WAVE and other applications for Linux, Mac OS X, and MS-Windows in the WFDB Software Package, also from PhysioNet
- For using the following EDF files in Python code, we suggest using the PyEDFlib for processing the files (https://github.com/holgern/pyedflib/tree/master/pyedflib).
Contributors
This database was contributed by Igor Zyma, Sergii Tukaev, and Ivan Seleznov, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Department of Electronic Engineering.
For further information, please contact:
Ivan Seleznov
National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Department of Electronic Engineering.
ivan.seleznov1@gmail.com
Files
Name Last modified Size Description
Parent Directory -
DOI 2019-02-07 09:43 19
Subject00_1.edf 2018-12-05 06:00 3.7M
subject-info.csv 2018-12-12 16:41 1.0K
RECORDS 2018-12-12 16:39 1.1K list of record names
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PhysioNet is supported by the National Institute of General Medical Sciences (NIGMS) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number 2R01GM104987-09.
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