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High-density scalp electroencephalogram dataset during sensorimotor rhythm-based brain-computer interfacing.

Seitaro Iwama1, Masumi Morishige2, Midori Kodama2

  • 1Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, Tokyo, Kanagawa, Japan.

Scientific Data
|June 15, 2023
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Summary

This study provides electroencephalogram (EEG) data for brain-computer interface (BCI) research, focusing on sensorimotor rhythm (SMR) neurofeedback. The dataset aids in understanding factors influencing BCI learning efficiency and variability.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Brain-computer interfaces (BCIs) leverage neurofeedback for voluntary control of neural activity.
  • Scalp electroencephalogram (EEG) is commonly used to probe motor cortical activities for BCI applications.
  • Variability in BCI learning remains a challenge, influenced by neurophysiological and experimental factors.

Purpose of the Study:

  • To provide a comprehensive EEG dataset for analyzing brain-computer interface (BCI) learning.
  • To investigate factors contributing to variability in BCI performance using sensorimotor rhythm (SMR).
  • To facilitate research into optimizing BCI learning efficiency.

Main Methods:

  • Acquisition of high-density (128-channel) scalp EEG data from participants using BCIs.
  • Utilized sensorimotor rhythm (SMR) neurofeedback based on motor imagery of right-hand movement.
  • Employed event-related desynchronization (ERD) as the control strategy for SMR-based BCIs.

Main Results:

  • The study presents four distinct EEG datasets collected during BCI operation.
  • Data captures neural activity related to motor imagery and SMR modulation.
  • The dataset is structured to enable exploration of BCI learning variability.

Conclusions:

  • This dataset offers a valuable resource for researchers studying BCI learning.
  • It enables investigation into the sources of variability in BCI performance.
  • Facilitates the development of more effective BCI systems and training paradigms.