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

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.