BCI-Utility Metric for Asynchronous P300 Brain-Computer Interface Systems.
BCI-Utility Metric for Asynchronous P300 Brain-Computer Interface Systems.
复制标题
BCI-用于异步P300脑机接口系统的效用度量。
DOI：
10.1109/tnsre.2023.3322125
复制
发表时间：
2023
影响因子：
4.9
通讯作者：
Huggins, Jane E.
中科院分区：
文献类型：
--
作者：
Ma, Guoxuan;Kang, Jian;Thompson, David E.;Huggins, Jane E.
脑机接口（BCI）被设想为有严重运动障碍的人的辅助技术选择。传统的同步事件相关电位（ERP）BCI设计使用固定的通信速度，容易受到注意力变化的影响。最近的ERP BCI设计增加了异步功能，包括暂停和动态停止，但如何评估异步BCI性能仍然是一个悬而未决的问题。在这项工作中，我们建立在BCI效用指标，以创建第一个评估指标，特别考虑到异步功能的自定进度的BCI。该度量将准确性视为以下三个中的所有一个：当选择是有意的时正确选择的概率、当选择是有意的时做出选择的概率、以及当选择是有意的时放弃的概率。此外，它还考虑了使用动态停止时选择所需的平均时间以及打算选择与弃权的比例。我们建立了衍生度量的有效性，通过广泛的模拟，并说明和讨论其实际使用的真实世界的BCI数据。我们描述了不同的输入与BCI效用曲线图在不同的参数设置下的相对贡献。通常，BCI效用度量随着任何准确度值的增加而增加，并且随着预期选择的预期时间的增加而减小。此外，在许多情况下，我们发现缩短预期选择的预期时间是提高BCI效用的最有效方法，这需要能够准确预测和动态停止的异步BCI系统的进步。
The Brain-Computer Interface (BCI) was envisioned as an assistive technology option for people with severe movement impairments. The traditional synchronous event-related potential (ERP) BCI design uses a fixed communication speed and is vulnerable to variations in attention. Recent ERP BCI designs have added asynchronous features, including abstention and dynamic stopping, but it remains a open question of how to evaluate asynchronous BCI performance. In this work, we build on the BCI-Utility metric to create the first evaluation metric with special consideration of the asynchronous features of self-paced BCIs. This metric considers accuracy as all of the following three – probability of a correct selection when a selection was intended, probability of making a selection when a selection was intended, and probability of an abstention when an abstention was intended. Further, it considers the average time required for a selection when using dynamic stopping and the proportion of intended selections versus abstentions. We establish the validity of the derived metric via extensive simulations, and illustrate and discuss its practical usage on real-world BCI data. We describe the relative contribution of different inputs with plots of BCI-Utility curves under different parameter settings. Generally, the BCI-Utility metric increases as any of the accuracy values increase and decreases as the expected time for an intended selection increases. Furthermore, in many situations, we find shortening the expected time of an intended selection is the most effective way to improve the BCI-Utility, which necessitates the advancement of asynchronous BCI systems capable of accurate abstention and dynamic stopping.