Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 30072, People's Republic of China.
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.
J Neural Eng. 2022 Apr 12;19(2). doi: 10.1088/1741-2552/ac60c8.
A P300-brain computer interface (P300-BCI) conveys a subject's intention through recognition of their event-related potentials (ERPs). However, in the case of visual stimuli, its performance depends strongly on eye gaze. When eye movement is impaired, it becomes difficult to focus attention on a target stimulus, and the quality of the ERP declines greatly, thereby affecting recognition efficiency.In this paper, the expectancy wave (E-wave) is proposed to improve signal quality and thereby improve identification of visual targets under the covert attention. The stimuli of the P300-BCI described here are presented in a fixed sequence, so the subjects can predict the next target stimulus and establish a stable expectancy effect of the target stimulus through training. Features from the E-wave that occurred 0 ∼ 300 ms before a stimulus were added to the post-stimulus ERP components for intention recognition.Comparisons of ten healthy subjects before and after training demonstrated that the expectancy wave generated before target stimulus could be used with the P300 component to improve character recognition accuracy (CRA) from 85% to 92.4%. In addition, CRA using only the expectancy component can reach 68.2%, which is significantly greater than random probability (16.7%). The results of this study indicate that the expectancy wave can be used to improve recognition efficiency for a gaze-independent P300-BCI, and that training contributes to induction and recognition of the potential.This study proposes an effective approach to an efficient gaze-independent P300-BCI system.
P300 脑机接口（P300-BCI）通过识别事件相关电位（ERP）来传达主体的意图。然而，在视觉刺激的情况下，其性能强烈依赖于眼球注视。当眼球运动受损时，很难将注意力集中在目标刺激上，并且 ERP 的质量会大大下降，从而影响识别效率。
在本文中，提出了期望波（E-wave）来改善信号质量，从而提高在隐蔽注意力下对视觉目标的识别。这里描述的 P300-BCI 的刺激以固定顺序呈现，因此受试者可以通过训练来预测下一个目标刺激，并建立目标刺激的稳定期望效应。在刺激之前 0∼300 毫秒出现的 E 波特征被添加到刺激后 ERP 成分中，用于意图识别。
十位健康受试者在训练前后的比较表明，在目标刺激之前产生的期望波可以与 P300 成分一起使用，将字符识别准确率（CRA）从 85%提高到 92.4%。此外，仅使用期望成分的 CRA 可以达到 68.2%，显著高于随机概率（16.7%）。
这项研究的结果表明，期望波可用于提高对独立于注视的 P300-BCI 的识别效率，并且训练有助于潜在的诱导和识别。
这项研究提出了一种有效的方法来实现高效的独立于注视的 P300-BCI 系统。
