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P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
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A high-speed brain speller using steady-state visual evoked potentials.

Masaki Nakanishi1, Yijun Wang, Yu-Te Wang

  • 1Graduate School of Science and Technology, Keio University, Yokohama, Kanagawa, 223-8522, Japan.

International Journal of Neural Systems
|August 2, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a high-speed brain-computer interface (BCI) speller using steady-state visual evoked potentials (SSVEP). It achieves a record information transfer rate for electroencephalogram (EEG)-based BCIs, showing potential for real-world use.

Keywords:
Steady-state visual evoked potentialbrain-computer interfacemixed frequency and phase codingspeller

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Brain-computer interfaces (BCIs) using steady-state visual evoked potentials (SSVEP) face challenges in stimulus presentation and target identification for complex spelling.
  • Existing SSVEP spellers are limited by monitor refresh rates, restricting the number of available targets.

Purpose of the Study:

  • To explore the feasibility of a high-speed SSVEP speller utilizing mixed frequency and phase coding on a standard computer monitor.
  • To develop a novel approach for target identification in SSVEP-based BCIs.

Main Methods:

  • A frequency and phase approximation approach was used to create 32 unique flickers by combining eight frequencies (8-15 Hz) and four phases (0°, 90°, 180°, 270°).
  • A multi-channel approach employing Canonical Correlation Analysis (CCA) and SSVEP training data was implemented for target identification.
  • A simulated online experiment was conducted with 13 subjects.

Main Results:

  • The developed SSVEP speller achieved a spelling rate of 40 characters per minute.
  • An average information transfer rate (ITR) of 166.91 bits/min was recorded across all subjects.
  • A maximum individual ITR of 192.26 bits/min was achieved, representing the highest reported ITR for electroencephalogram (EEG)-based BCIs.

Conclusions:

  • The proposed mixed frequency and phase coding strategy effectively overcomes limitations imposed by monitor refresh rates.
  • The high ITR achieved demonstrates the significant potential of this high-speed SSVEP-based BCI for practical applications.
  • This study paves the way for more efficient and accessible brain-computer interfaces.