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Efficient CSP Algorithm With Spatio-Temporal Filtering for Motor Imagery Classification
IEEE Transactions on Neural Systems and Rehabilitation Engineering ( IF 4.9 ) Pub Date : 2020-03-09 , DOI: 10.1109/tnsre.2020.2979464
Aimin Jiang , Jing Shang , Xiaofeng Liu , Yibin Tang , Hon Keung Kwan , Yanping Zhu

Common spatial pattern (CSP) is an efficient algorithm widely used in feature extraction of EEG-based motor imagery classification. Traditional CSP depends only on spatial filtering, that aims to maximize or minimize the ratio of variances of filtered EEG signals in different classes. Recent advances of CSP approaches show that temporal filtering is also preferable to extract discriminative features. In view of this perspective, a novel spatio-temporal filtering strategy is proposed in this paper. To improve computational efficiency and alleviate the overfitting issue frequently encountered in the case of small sample size, the same temporal filter is designed by EEG signals of the same class and shared by all the spatial channels. Spatial and temporal filters can be updated alternatively in practice. Furthermore, each of the resulting designs can still be cast as a CSP problem and tackled efficiently by the eigenvalue decomposition. To alleviate the adverse effects of outliers or noisy EEG channels, sparse spatial or temporal filters can also be achieved by incorporating an $\ell _{{1}}$ -norm-based regularization term in our CSP problem. The regularized spatial or temporal filter design is iteratively reformulated as a CSP problem via the reweighting technique. Two sets of motor imagery EEG data of BCI competitions are used in our experiments to verify the effectiveness of the proposed algorithm.

中文翻译:

时空滤波的高效CSP算法在运动图像分类中的应用

通用空间模式(CSP)是一种有效的算法,广泛用于基于EEG的运动图像分类的特征提取。传统的CSP仅依赖于空间滤波,其目的是最大化或最小化不同类别中经滤波的EEG信号的方差比。CSP方法的最新进展表明,时域滤波也比提取区分特征更可取。鉴于此,本文提出了一种新颖的时空滤波策略。为了提高计算效率并减轻在小样本量情况下经常遇到的过度拟合问题,由相同类别的EEG信号设计了相同的时间滤波器,并由所有空间通道共享。在实践中,可以交替更新空间和时间过滤器。此外,每个结果设计仍然可以被视为CSP问题,并通过特征值分解有效解决。为了减轻离群值或嘈杂的EEG通道的不利影响,还可以通过合并一个 $ \ ell _ {{1}} $ CSP问题中基于-norm的正则化术语。通过重加权技术,将规则化的空间或时间滤波器设计迭代地重新构造为CSP问题。我们在实验中使用了两组BCI竞赛的运动图像EEG数据来验证所提出算法的有效性。
更新日期:2020-04-22
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