Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia.
J Neural Eng. 2021 Mar 1;18(2). doi: 10.1088/1741-2552/abd51f.
. The common spatial patterns (CSP) algorithm is an effective method to extract discriminatory features from electroencephalography (EEG) to be used by a brain-computer interface (BCI). However, informed selection of CSP filters typically requires oversight from a BCI expert to accept or reject filters based on the neurophysiological plausibility of their activation patterns. Our goal was to identify, analyze and automatically classify prototypical CSP patterns to enhance the prediction of motor imagery states in a BCI.. A data-driven approach that used four publicly available EEG datasets was adopted. Cluster analysis revealed recurring, visually similar CSP patterns and a convolutional neural network was developed to distinguish between established CSP pattern classes. Furthermore, adaptive spatial filtering schemes that utilize the categorization of CSP patterns were proposed and evaluated.. Classes of common neurophysiologically probable and improbable CSP patterns were established. Analysis of the relationship between these categories of CSP patterns and classification performance revealed discarding neurophysiologically improbable filters can decrease decoder performance. Further analysis revealed that the spatial orientation of EEG modulations can evolve over time, and that the features extracted from the original CSP filters can become inseparable. Importantly, it was shown through a novel adaptive CSP technique that adaptation in response to these emerging patterns can restore feature separability.. These findings highlight the importance of considering and reporting on spatial filter activation patterns in both online and offline studies. They also emphasize to researchers in the field the importance of spatial filter adaptation in BCI decoder design, particularly for online studies with a focus on training users to develop stable and suitable brain patterns.
. 共空间模式（CSP）算法是一种从脑电图（EEG）中提取鉴别特征的有效方法，可用于脑机接口（BCI）。然而，CSP 滤波器的信息选择通常需要 BCI 专家的监督，根据其激活模式的神经生理学合理性来接受或拒绝滤波器。我们的目标是识别、分析和自动分类原型 CSP 模式，以增强 BCI 中运动想象状态的预测。. 采用了一种基于数据驱动的方法，使用了四个公开的 EEG 数据集。聚类分析揭示了反复出现的、视觉上相似的 CSP 模式，并且开发了卷积神经网络来区分已建立的 CSP 模式类别。此外，提出并评估了利用 CSP 模式分类的自适应空间滤波方案。. 建立了常见神经生理学上可能和不可能的 CSP 模式的类别。分析这些 CSP 模式类别与分类性能之间的关系表明，丢弃神经生理学上不可能的滤波器会降低解码器的性能。进一步的分析表明，EEG 调制的空间方向可以随时间演变，并且从原始 CSP 滤波器中提取的特征可能变得不可分离。重要的是，通过一种新的自适应 CSP 技术表明，针对这些新出现的模式进行自适应可以恢复特征的可分离性。. 这些发现强调了在在线和离线研究中都要考虑和报告空间滤波器激活模式的重要性。它们还向该领域的研究人员强调了在 BCI 解码器设计中适应空间滤波器的重要性，特别是对于专注于培训用户开发稳定和合适的大脑模式的在线研究。
