College of Communication Engineering, Jilin University, Changchun 130012, China.
College of Communication Engineering, Jilin University, Changchun 130012, China.
Comput Biol Med. 2024 Jul;177:108619. doi: 10.1016/j.compbiomed.2024.108619. Epub 2024 May 20.
In order to improve the performance of binary motor imagery (MI) - based brain-computer interfaces (BCIs) using electroencephalography (EEG), a novel method (PSS-CSP) is proposed, which combines spectral subtraction with common spatial pattern. Spectral subtraction is an effective denoising method which is initially adopted to process MI-based EEG signals for binary BCIs in this work. On this basis, we proposed a novel feature extraction method called power spectral subtraction-based common spatial pattern (PSS-CSP) , which calculates the differences in power spectrum between binary classes of EEG signals and uses the differences in the feature extraction process. Additionally, support vector machine (SVM) algorithm is used for signal classification. Results show the proposed method (PSS-CSP) outperforms certain existing methods, achieving a classification accuracy of 76.8% on the BCIIV dataset 2b, and 76.25% and 77.38% on the OpenBMI dataset session 1 and session 2, respectively.
为了提高基于脑电的二进制运动想象（MI）脑机接口（BCI）的性能，提出了一种新的方法（PSS-CSP），该方法结合了频谱减法和共空间模式。频谱减法是一种有效的去噪方法，最初被用于处理基于 MI 的 EEG 信号，用于二进制 BCI。在此基础上，我们提出了一种新的特征提取方法，称为基于功率谱减法的共空间模式（PSS-CSP），它计算 EEG 信号的二进制类之间的功率谱差异，并在特征提取过程中使用差异。此外，支持向量机（SVM）算法用于信号分类。结果表明，所提出的方法（PSS-CSP）优于某些现有的方法，在 BCIIV 数据集 2b 上达到了 76.8%的分类准确率，在 OpenBMI 数据集 session1 和 session2 上分别达到了 76.25%和 77.38%。
