在BCI中生成用于拼写的最佳刺激序列的活动RBSE框架
An Active RBSE Framework to Generate Optimal Stimulus Sequences in a BCI for Spelling
摘要：
一类脑机接口（BCI）采用脑电图（EEG）信号的非侵入性记录，以使具有严重言语和运动障碍的用户能够与他们的环境和社交网络交互。例如，用于分型的基于EEG的BCI普遍利用事件相关电位（ERP）进行推断。当前基于ERP的逐个字母键入的BCI中的呈现范式设计通常使用任意子集字符来查询用户。然而，打字准确性和打字速度可以通过更明智的子集选择和闪存分配来潜在地增强。在这篇手稿中，我们介绍了主动递归贝叶斯状态估计（主动RBSE）框架的推理和序列优化。在每次迭代中呈现之前，开发的框架基于查询函数最优地选择子集，而不是显示随机选择的字符的子集。在每次意图检测期间，使所选择的查询自适应地专门用于用户。通过一项基于模拟的研究，我们评估了主动RBSE在打字速度和准确性方面对语言模型辅助打字BCI性能的影响。为了提供一个比较的基线，我们还利用标准的演示范式，即行和列矩阵演示范式，也随机快速串行视觉演示范式。结果表明，利用主动RBSE可以提高系统的在线性能，无论是在打字的准确性和速度。
英文摘要：
A class of brain computer interfaces (BCIs) employs noninvasive recordings of electroencephalography (EEG) signals to enable users with severe speech and motor impairments to interact with their environment and social network. For example, EEG based BCIs for typing popularly utilize event related potentials (ERPs) for inference. Presentation paradigm design in current ERP-based letter by letter typing BCIs typically query the user with an arbitrary subset characters. However, the typing accuracy and also typing speed can potentially be enhanced with more informed subset selection and flash assignment. In this manuscript, we introduce the active recursive Bayesian state estimation (active-RBSE) framework for inference and sequence optimization. Prior to presentation in each iteration, rather than showing a subset of randomly selected characters, the developed framework optimally selects a subset based on a query function. Selected queries are made adaptively specialized for users during each intent detection. Through a simulation-based study, we assess the effect of active-RBSE on the performance of a language-model assisted typing BCI in terms of typing speed and accuracy. To provide a baseline for comparison, we also utilize standard presentation paradigms namely, row and column matrix presentation paradigm and also random rapid serial visual presentation paradigms. The results show that utilization of active-RBSE can enhance the online performance of the system, both in terms of typing accuracy and speed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
