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Motion artifact contaminated multichannel EEG dataset.

Sheikh Farhana Binte Ahmed1, Md Ruhul Amin1, Md Kafiul Islam2

  • 1Islamic University of Technology, Gazipur, Bangladesh.

Data in Brief
|November 4, 2024
PubMed
Summary

This study introduces a new open-access dataset of electroencephalography (EEG) recordings with motion artifacts. This resource aims to advance the development of wearable EEG devices by enabling research into artifact removal techniques.

Keywords:
Ambulatory EEGDatasetElectroencephalography/electroencephalogram (EEG)Human physical movementMobile EEGMotion artifactsWearable EEG

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Signal Processing

Background:

  • Wearable electroencephalography (EEG) is susceptible to motion artifacts in ambulatory settings.
  • The lack of open-access datasets hinders the development of robust wearable EEG applications for artifact removal.
  • Motion artifacts contaminate EEG signals, complicating analysis and reducing the reliability of brain activity measurements.

Purpose of the Study:

  • To present a novel, open-access dataset of multi-channel EEG recordings.
  • To capture EEG data during various physical movements to simulate real-world ambulatory conditions.
  • To facilitate research on detecting and mitigating motion artifacts in wearable EEG.

Main Methods:

  • Recorded 14-channel EEG data from a single healthy male subject using an EMOTIV EPOCH headset.
  • Collected data during a variety of upper-body, lower-body, and full-body movements, including eye and head motions, walking, and transitions between sitting and standing.
  • Utilized MATLAB for data visualization and conversion from CSV to .mat format, including motion sensor data.

Main Results:

  • Successfully generated a comprehensive dataset of EEG and motion sensor recordings.
  • The dataset includes diverse movement artifacts relevant to ambulatory EEG scenarios.
  • The data is structured for easy access and analysis, with 14 channels of EEG and 9 channels of motion data.

Conclusions:

  • The presented open-access dataset is a valuable resource for the scientific community.
  • It will accelerate the development of advanced signal processing algorithms for wearable EEG.
  • This dataset supports research aimed at improving the accuracy and usability of mobile brain-computer interfaces.