Song Xiaomu, Yoon Suk-Chung
Department of Electrical Engineering, School of Engineering, Widener University, Chester, PA 19013, USA.
Department of Computer Science, College of Arts and Sciences, Widener University, Chester, PA 19013, USA.
Comput Biol Med. 2015 Jun;61:150-60. doi: 10.1016/j.compbiomed.2015.03.023. Epub 2015 Mar 28.
Common Spatial Patterns (CSP) is a widely used spatial filtering technique for electroencephalography (EEG)-based brain-computer interface (BCI). It is a two-class supervised technique that needs subject-specific training data. Due to EEG nonstationarity, EEG signal may exhibit significant intra- and inter-subject variation. As a result, spatial filters learned from a subject may not perform well for data acquired from the same subject at a different time or from other subjects performing the same task. Studies have been performed to improve CSP's performance by adding regularization terms into the training. Most of them require target subjects' training data with known class labels. In this work, an adaptive CSP (ACSP) method is proposed to analyze single trial EEG data from single and multiple subjects. The method does not estimate target data's class labels during the adaptive learning and updates spatial filters for both classes simultaneously. The proposed method was evaluated based on a comparison study with the classic CSP and several CSP-based adaptive methods using motor imagery EEG data from BCI competitions. Experimental results indicate that the proposed method can improve the classification performance as compared to the other methods. For circumstances where true class labels of target data are not instantly available, it was examined if adding classified target data to training data would improve the ACSP learning. Experimental results show that it would be better to exclude them from the training data. The proposed ACSP method can be performed in real-time and is potentially applicable to various EEG-based BCI applications.
公共空间模式（CSP）是一种广泛应用于基于脑电图（EEG）的脑机接口（BCI）的空间滤波技术。它是一种两类监督技术，需要特定受试者的训练数据。由于脑电图的非平稳性，脑电图信号可能会在受试者内部和受试者之间表现出显著的变化。因此，从一个受试者身上学习到的空间滤波器可能对在不同时间从同一受试者获取的数据或对执行相同任务的其他受试者的数据表现不佳。已经进行了一些研究，通过在训练中添加正则化项来提高CSP的性能。其中大多数需要具有已知类别标签的目标受试者的训练数据。在这项工作中，提出了一种自适应CSP（ACSP）方法来分析来自单个和多个受试者的单次试验脑电图数据。该方法在自适应学习过程中不估计目标数据的类别标签，而是同时更新两类的空间滤波器。基于与经典CSP和几种基于CSP的自适应方法的比较研究，使用来自BCI竞赛的运动想象脑电图数据对所提出的方法进行了评估。实验结果表明，与其他方法相比，所提出的方法可以提高分类性能。对于目标数据的真实类别标签不能立即获得的情况，研究了将分类后的目标数据添加到训练数据中是否会改善ACSP学习。实验结果表明，最好将它们从训练数据中排除。所提出的ACSP方法可以实时执行，并且潜在地适用于各种基于脑电图的BCI应用。
