基于闪光与运动混合编码的脑机接口方法
Brain-computer interface method based on light-flashing and motion hybrid coding.
作者信息
Yan Wenqiang, Xu Guanghua
机构信息
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.
出版信息
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。
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