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A Cross-Session Dataset for Collaborative Brain-Computer Interfaces Based on Rapid Serial Visual Presentation.

Li Zheng1,2, Sen Sun3, Hongze Zhao1

  • 1State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China.

Frontiers in Neuroscience
|November 16, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new dataset for collaborative Brain-Computer Interfaces (BCIs) using rapid serial visual presentation (RSVP). Collaborative BCIs significantly improve performance over individual systems by fusing multi-subject brain activity.

Keywords:
brain-computer interfaces (BCI)collaborative BCIcross-session transferelectroencephalogram (EEG)event related potentials (ERP)rapid serial visual presentation (RSVP)

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Brain-computer interfaces (BCIs) using rapid serial visual presentation (RSVP) are effective for image categorization but face challenges in single-trial event-related potential (ERP) detection from electroencephalography (EEG).
  • EEG signal variability complicates long-term BCI calibration, necessitating robust, cross-session algorithms.
  • Collaborative BCIs, which fuse brain activity from multiple subjects, offer a promising approach to enhance BCI performance.

Purpose of the Study:

  • To present a novel cross-session EEG dataset for a collaborative RSVP-based BCI system.
  • To facilitate the development of advanced signal processing and classification algorithms for improved BCI performance and practicality.
  • To address the current lack of publicly available datasets for collaborative RSVP-BCI research.

Main Methods:

  • Collected cross-session EEG data from 14 subjects performing synchronous, collaborative RSVP-based target image detection tasks in groups of two.
  • Subjects participated in two experimental sessions with an average interval of approximately 23 days.
  • Data was evaluated to assess the efficacy of signal processing algorithms in enhancing cross-session BCI performance.

Main Results:

  • Adequate signal processing algorithms significantly improve cross-session BCI performance in both individual and collaborative settings.
  • Collaborative BCI methods, by fusing information from multiple subjects, demonstrate significantly enhanced performance compared to individual BCIs.
  • The developed dataset supports research into more efficient algorithms for practical collaborative RSVP-BCI systems.

Conclusions:

  • The presented dataset is valuable for advancing research in collaborative RSVP-based BCIs.
  • Cross-session algorithms are crucial for enhancing the performance and usability of BCIs over time.
  • Fusing multi-subject brain activity in collaborative BCIs leads to substantial performance gains.