Memmott Tab, Klee Daniel, Smedemark-Margulies Niklas, Oken Barry
Department of Neurology, Oregon Health & Science University, Portland, OR, United States.
Institute on Development and Disability, Oregon Health & Science University, Portland, OR, United States.
Front Hum Neurosci. 2025 Mar 4;19:1551214. doi: 10.3389/fnhum.2025.1551214. eCollection 2025.
A significant challenge in developing reliable Brain-Computer Interfaces (BCIs) is the presence of artifacts in the acquired brain signals. These artifacts may lead to erroneous interpretations, poor fitting of models, and subsequent reduced online performance. Furthermore, BCIs in a home or hospital setting are more susceptible to environmental noise. Artifact handling procedures aim to reduce signal interference by filtering, reconstructing, and/or eliminating unwanted signal contaminants. While straightforward conceptually and largely undisputed as essential, suitable artifact handling application in BCI systems remains unsettled and may reduce performance in some cases. A potential confound that remains unexplored in the majority of BCI studies using these procedures is the lack of parity with online usage (e.g., online parity). This manuscript compares classification performance between frequently used offline digital filtering, using the whole dataset, and an online digital filtering approach where the segmented data epochs that would be used during closed-loop control are filtered instead. In a sample of healthy adults ( = 30) enrolled in a BCI pilot study to integrate new communication interfaces, there were significant benefits to model performance when filtering with online parity. While online simulations indicated similar performance across conditions in this study, there appears to be no drawback to the approach with greater online parity.
开发可靠的脑机接口（BCI）面临的一个重大挑战是采集到的脑信号中存在伪迹。这些伪迹可能导致错误的解读、模型拟合不佳以及随后在线性能的下降。此外，家庭或医院环境中的脑机接口更容易受到环境噪声的影响。伪迹处理程序旨在通过滤波、重建和/或消除不需要的信号污染物来减少信号干扰。虽然在概念上很简单，并且作为必不可少的环节在很大程度上没有争议，但在脑机接口系统中合适的伪迹处理应用仍未确定，并且在某些情况下可能会降低性能。在大多数使用这些程序的脑机接口研究中，一个尚未探索的潜在混淆因素是与在线使用缺乏一致性（例如，在线一致性）。本手稿比较了使用整个数据集的常用离线数字滤波和在线数字滤波方法之间的分类性能，在线数字滤波方法是对闭环控制期间将使用的分段数据段进行滤波。在一项纳入30名健康成年人的脑机接口试点研究样本中，该研究旨在整合新的通信接口，采用具有在线一致性的滤波方法时，模型性能有显著提升。虽然在线模拟表明本研究中各条件下的性能相似，但采用具有更高在线一致性的方法似乎没有缺点。
