The set of 22 sessions of 14-channel eeg signals recorded during watching pictures - Open Research Data - Bridge of Knowledge

Your account

login

Search

The set of 22 sessions of 14-channel eeg signals recorded during watching pictures

Description

The data were collected in order to perform research on the possibility of controlling the content displayed on the monitor screen using human emotional states extracted from EEG signals. The dataset contains recordings of 14-channel EEG signals collected from 10 persons within 22 sessions, during which 45 different random photos taken from the ImageNet (http://www.image-net.org/) collection were presented. All sessions were collected using the Emotiv Epoc+ device, which is equipped with 14 electrodes arranged in the 10-20 standard system.

The data for each sessions are collected in two files: sn_emotion_picture_timestamp.csv and sn_EEGlogger.csv, where n stands for the number of session. The time-dependent raw EEG signals for all electrodes (AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4) are written in the sn_EEGlogger files, while the timestamps of displaying pictures, self-assessment of the emotional states and ImageNet pictures ID are in the sn_emotion_picture_timestamp files. The display time of the pictures within first 13 sessions varies from 2 to 10 seconds (five pictures per each time bin), whereas in the last 9 sessions is constant and equals 5 seconds. During sessions from 14 to 22, the brain waves (theta, alpha, low beta, high beta and gamma) are additionally calculated based on the raw EEG signals for all electrodes and written to the sn_brain_waves.csv files.

Dataset file

EEG_sessions_Kastrau_Jasik.zip
54.1 MB, S3 ETag 572817048e6fa8ea3f82eba50eac4ad8-4, downloads: 66
The file hash is calculated from the formula
hexmd5(md5(part1)+md5(part2)+...)-{parts_count} where a single part of the file is 512 MB in size.

Example script for calculation:
https://github.com/antespi/s3md5
download file EEG_sessions_Kastrau_Jasik.zip

File details

License:
Creative Commons: by-nc-sa 4.0 open in new tab
CC BY-NC-SA
Non-commercial - Share-alike

Details

Year of publication:
2019
Verification date:
2020-12-17
Creation date:
2018
Dataset language:
English
Fields of science:
  • biomedical engineering (Engineering and Technology)
  • physical sciences (Natural sciences)
DOI:
DOI ID 10.34808/1e5c-pp74 open in new tab
Verified by:
Gdańsk University of Technology

Keywords

Cite as

Authors

seen 375 times