Yan Wenqiang, Xu Guanghua
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.
State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, China.
Cogn Neurodyn. 2020 Oct;14(5):697-708. doi: 10.1007/s11571-020-09616-3. Epub 2020 Jul 16.
The human best response frequency band for steady-state visual evoked potential stimulus is limited. This results in a reduced number of encoded targets. To circumvent this, we proposed a brain-computer interface (BCI) method based on light-flashing and motion hybrid coding. The hybrid paradigm pattern consisted of a circular light-flashing pattern and a motion pattern located in the inner ring of light-flashing pattern. The motion and light-flashing patterns had different frequencies. This study used five frequencies to encode nine targets. The motion frequency and the light-flashing frequency of the hybrid paradigm consisted of two frequencies in five frequencies. The experimental results showed that the hybrid paradigm could induce stable motion frequency, light-flashing frequency and its harmonic components. Moreover, the modulation between motion and light-flashing was weak. The average accuracy was 92.96% and the information transfer rate was 26.10 bits/min. The experimental results showed that the proposed method could be considered for practical BCI systems.
人类对稳态视觉诱发电位刺激的最佳反应频段是有限的。这导致编码目标数量减少。为了规避这一问题，我们提出了一种基于闪光和运动混合编码的脑机接口（BCI）方法。混合范式模式由圆形闪光模式和位于闪光模式内环的运动模式组成。运动模式和闪光模式具有不同的频率。本研究使用五个频率对九个目标进行编码。混合范式的运动频率和闪光频率由五个频率中的两个频率组成。实验结果表明，混合范式能够诱导出稳定的运动频率、闪光频率及其谐波成分。此外，运动和闪光之间的调制较弱。平均准确率为92.96%，信息传输率为26.10比特/分钟。实验结果表明，所提出的方法可应用于实际的BCI系统。
