University of Applied Sciences Zwickau, Faculty of Physical Engineering/Computer Sciences, Zwickau, Germany.
University Leipzig, Department of Diagnostic and Interventional Radiology, Leipzig, Germany.
Ger Med Sci. 2024 Sep 25;22:Doc08. doi: 10.3205/000334. eCollection 2024.
Common spatial pattern (CSP) is a common filter technique used for pre-processing of electroencephalography (EEG) signals for imaginary movement classification tasks. It is crucial to reduce the amount of features especially in cases where few data is available. Therefore, different approaches to reduce the amount of electrodes used for CSP calculation are tried in this research.
Freely available EEG datasets are used for the evaluation. To evaluate the approaches a simple classification pipeline consisting mainly of the CSP calculation and linear discriminant analysis for classification is used. A baseline over all electrodes is calculated and compared against the results of the approaches.
The most promising approach is to use the ability of CSP to provide information about the origin of the created filter. An algorithm that extracts the important electrodes from the CSP utilizing these information is proposed.The results show that using subject specific electrode positions has a positive impact on accuracy for the classification task. Further, it is shown that good performing electrode combinations in one session are not necessarily good performing electrodes in another session of the same subject. In addition to the combinations calculated using the developed algorithm, 26 additional electrode combinations are proposed. These can be taken into account when selecting well-performing electrode combinations. In this research we could achieve an accuracy improvement of over 10%.
Carefully selecting the correct electrode combination can improve accuracy for classifying an imaginary movement task.
共空间模式（CSP）是一种常用的滤波器技术，用于对想象运动分类任务的脑电图（EEG）信号进行预处理。减少特征数量尤其重要，特别是在数据较少的情况下。因此，本研究尝试了不同的方法来减少用于 CSP 计算的电极数量。
使用免费提供的 EEG 数据集进行评估。为了评估这些方法，使用了一个简单的分类管道，主要由 CSP 计算和线性判别分析组成。计算了所有电极的基线，并与这些方法的结果进行了比较。
最有前途的方法是利用 CSP 提供有关创建滤波器起源的信息的能力。提出了一种利用这些信息从 CSP 中提取重要电极的算法。结果表明，使用特定于主题的电极位置对分类任务的准确性有积极影响。此外，还表明在同一主题的一次会话中表现良好的电极组合在另一次会话中不一定表现良好。除了使用开发的算法计算的组合外，还提出了 26 个额外的电极组合。在选择表现良好的电极组合时，可以考虑这些组合。在这项研究中，我们可以将准确性提高 10%以上。
仔细选择正确的电极组合可以提高想象运动任务分类的准确性。
