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Longitudinal multitask wireless electrocorticography data from two fully implanted nonhuman primates.

Huixiang Yang1, Ryohei Fukuma1,2, Tomoyuki Namima3,4

  • 1Department of Neuroinformatics, The University of Osaka Graduate School of Medicine, Suita, Japan.

Scientific Data
|December 11, 2025
PubMed
Summary
This summary is machine-generated.

This study provides long-term wireless electrocorticography (ECoG) data from nonhuman primates, detailing cortical dynamics during various tasks. The open-access dataset supports brain-computer interface (BCI) research and understanding of neural activity over time.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Data Science

Background:

  • Chronic electrocorticography (ECoG) recordings are crucial for understanding long-term brain dynamics.
  • Existing datasets often lack the duration or wireless capabilities needed for comprehensive research.
  • Nonhuman primate models offer valuable insights into sensorimotor cortical functions.

Purpose of the Study:

  • To present a unique, high-quality dataset of chronic wireless ECoG recordings.
  • To facilitate research into long-term cortical dynamics and brain-computer interfaces (BCI).
  • To ensure data interoperability and reusability through adherence to the Brain Imaging Data Structure (BIDS).

Main Methods:

  • Utilized bilateral subdural ECoG arrays in two Japanese macaques (Macaca fuscata).
  • Employed fully implantable wireless transmission units for data acquisition over hundreds of days.
  • Recorded data during resting-state, auditory, motor tasks, and somatosensory evoked potential paradigms.
  • Organized all data, including raw signals, annotations, and metadata, according to the BIDS extension for intracranial electrophysiology.

Main Results:

  • Confirmed data quality and signal stability through impedance monitoring and spectral analyses.
  • Demonstrated reliability and consistency of ECoG signals across the extensive recording period.
  • Provided a comprehensive dataset covering diverse behavioral and stimulus conditions.

Conclusions:

  • The open-access longitudinal wireless ECoG dataset is a valuable resource for neuroscience research.
  • This data will advance the understanding of chronic cortical dynamics.
  • The dataset is expected to accelerate progress in brain-computer interface (BCI) development and applications.