Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China.
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.
J Neural Eng. 2020 Oct 14;17(5):056022. doi: 10.1088/1741-2552/abb4a6.
The P300 speller is a classic brain-computer interface (BCI) paradigm that has the potential to restore impaired motor control function. However, previous studies have confirmed that the letter recognition accuracy (LRA) of the P300 speller is a challenge when performing other tasks.
To address this, we implemented a dynamic stopping strategy (DSS) to maintain the P300 speller LRA when performing multiple tasks simultaneously. Multiple tasks with dynamic workload levels were adopted to simulate the brain's other thinking activities while operating P300 speller. A Bayes-based DSS offline model was built in single-task (only P300 speller task) and an online P300 speller system was established to test the DSS algorithm feasibility in dual-task.
Online experimental results showed that the P300 speller with DSS could achieve a high LRA (96.9%) under dual-task, which was similar to single-task (98.7%, p = 0.126). Under dual-task, DSS dynamically adjusted the discriminant confidence according to the workload levels of the distraction tasks (correlation coefficient r = -0.68). Therefore, DSS can increase the repeated sequences to compensate for the reduction of P300 speller signal-to-noise ratio caused by parallel thinking activities. The average of repeated sequences increased significantly from 4.98 times under single-task to 6.22 times under dual-task (p < 0.005). These results indicated that the P300 speller feature is robust and the DSS model built in single-task maintained the applicability in various dual-tasks.
Overall, this study provides a basis for the implementation of laboratory-developed BCI in real-world environments.
P300 拼写器是一种经典的脑机接口（BCI）范式，它有可能恢复受损的运动控制功能。然而，之前的研究已经证实，当执行其他任务时，P300 拼写器的字母识别准确率（LRA）是一个挑战。
为了解决这个问题，我们实施了一种动态停止策略（DSS），以在同时执行多个任务时保持 P300 拼写器的 LRA。采用具有动态工作负载水平的多个任务来模拟大脑在操作 P300 拼写器时的其他思维活动。在单任务（仅 P300 拼写器任务）中建立了基于贝叶斯的 DSS 离线模型，并在在线 P300 拼写器系统中建立了 DSS 算法在双任务中的可行性测试。
在线实验结果表明，具有 DSS 的 P300 拼写器在双任务下可以实现高的 LRA（96.9%），与单任务（98.7%，p = 0.126）相似。在双任务下，DSS 根据分心任务的工作负载水平动态调整判别置信度（相关系数 r = -0.68）。因此，DSS 可以增加重复序列，以补偿由于并行思维活动引起的 P300 拼写器信号噪声比降低。与单任务相比，重复序列的平均值从 4.98 次显著增加到 6.22 次（p < 0.005）。这些结果表明，P300 拼写器特征具有鲁棒性，并且在单任务中建立的 DSS 模型在各种双任务中保持了适用性。
总的来说，本研究为在实际环境中实施实验室开发的 BCI 提供了基础。
