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A calibration-free c-VEP based BCI employing narrow-band random sequences
Li Zheng1, Yida Dong1,2, Sen Tian3
1Laboratory of Solid State Optoelectronics Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, People's Republic of China.
Journal of Neural Engineering
|March 21, 2024
Summary
This study introduces a novel calibration-free brain-computer interface (BCI) using code-modulated visual evoked potentials (c-VEP) and narrow-band random sequences. The new system achieves high performance, comparable to existing methods, and shows potential for large-scale applications.
Area of Science:
- Neuroscience and Biomedical Engineering
- Brain-Computer Interfaces (BCIs)
- Signal Processing
Background:
- Code-modulated visual evoked potential (c-VEP) based BCIs offer high encoding efficiency.
- Existing c-VEP BCIs often require lengthy calibration, limiting practical use, especially with more targets.
- There is a need for practical, calibration-free BCI systems for wider adoption.
Purpose of the Study:
- To introduce and evaluate a calibration-free c-VEP based BCI system.
- To utilize narrow-band random sequences for signal encoding.
- To assess the system's performance against traditional steady-state visual evoked potential (SSVEP) BCIs.
Main Methods:
- Developed a c-VEP encoding method using narrow-band random sequences modulated grid flashes.
- Employed filter-bank canonical correlation analysis (FBCCA) for calibration-free decoding with original sequence templates.
- Compared performance of narrow-band random sequences (15-25 Hz and 8-16 Hz) against SSVEP (8-15.8 Hz) in 35 subjects.
Main Results:
- Offline analysis confirmed strong correlation between c-VEPs and narrow-band random sequences.
- Optimized calibration-free system (NBRS-15) achieved an average ITR of 78.56 bits/min, comparable to SSVEP.
- Simulated 1000-target BCI achieved a high average ITR of 102.1 bits/min.
Conclusions:
- Introduced a novel, calibration-free c-VEP BCI system using narrow-band random sequences.
- The proposed system demonstrates high information transfer rates (ITR) and robustness.
- This approach shows significant potential for developing large-target and high-performance BCIs.
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