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Dual-layer electroencephalography data during real-world table tennis.

Amanda Studnicki1, Daniel P Ferris1

  • 1J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, United States.

Data in Brief
|January 30, 2024
PubMed
Summary

This study introduces a novel electroencephalography (EEG) dataset for neuroscience research in real-world settings. The dataset captures high-fidelity brain activity during table tennis, enabling advanced analysis of visuomotor tasks.

Keywords:
EEGElectrocortical dynamicsIndependent component analysisMobile Brain Body Imaging (MoBI)Sports neuroscience

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Area of Science:

  • Neuroscience
  • Sports Science
  • Data Science

Background:

  • Ecological validity is crucial for neuroscience research.
  • Electroencephalography (EEG) offers portable, high-temporal-resolution brain activity recording.
  • Table tennis presents a complex testbed for studying visuomotor control and cognitive processes.

Purpose of the Study:

  • To present a comprehensive, multi-modal EEG dataset collected during real-world table tennis play.
  • To facilitate neuroscience research by providing high-fidelity, artifact-minimized brain activity recordings.
  • To establish a standardized dataset adhering to the Brain Imaging Data Structure (BIDS) format for enhanced data sharing and reuse.

Main Methods:

  • Collected synchronized, multivariate time series data from 25 participants playing table tennis.
  • Utilized a dual-layer EEG system with 120 scalp and 120 noise electrodes.
  • Integrated electromyography (EMG) and inertial measurement units (IMUs) for comprehensive movement and event capture.
  • Included anatomical MRI scans and digitized electrode locations for source localization.
  • Provided anonymized video recordings with labeled hit events and associated Premiere project files.

Main Results:

  • Successfully acquired high-fidelity EEG data during dynamic whole-body activity (table tennis).
  • Developed a synchronized, multi-modal dataset including EEG, EMG, IMU, MRI, and video data.
  • Formatted the dataset according to the Brain Imaging Data Structure (BIDS) standard.

Conclusions:

  • The presented dataset significantly advances the potential for ecologically valid neuroscience research.
  • The dual-layer EEG approach effectively mitigates artifacts during complex physical tasks.
  • The BIDS-formatted, multi-modal dataset provides a valuable resource for studying sensorimotor control, decision-making, and cognitive processes in naturalistic environments.