Background and objective

In the brain computer interface (BCI) field, using sub-band common spatial pattern (SBCSP) and filter bank common spatial pattern (FBCSP) can improve the accuracy of classification by selection a specific frequency band. However, in the cross-subject classification, due to the individual differences between different subjects, the performance is limited.

Methods

This paper introduces the idea of transfer learning and presents the sub-band target alignment common spatial pattern (SBTACSP) method and applies it to the cross-subject classification of motor imagery (MI) EEG signals. First, the EEG signals are bandpass-filtered into multiple frequency bands (sub-band filtering). Subsequently, the source domain trails are aligned into the target domain space in each frequency band. The CSP algorithm is then employed to extract features among which more representative features are selected by the minimum redundancy maximum relevance (mRMR) approach from each sub-band. Then the features of all sub-bands are fused. Finally, conventional linear discriminant analysis (LDA) algorithm is used for MI classification.

Results

Our method is evaluated on Datasets Ⅱa and Ⅱb of the BCI Competition Ⅳ. Compared with six state-of-the-art algorithms, the proposed SBTACSP method performed relatively the best and achieved a mean classification accuracy of 75.15% and 66.85% in cross-subject classification of Datasets Ⅱa and Ⅱb respectively.

Conclusion

Therefore, the combination of sub-band filtering and transfer learning achieves superior classification performance compared to either one. The proposed algorithms will greatly promote the practical application of MI based BCIs.

中文翻译：

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脑机接口中子带目标对准的公共空间模式

背景和目的

在脑机接口（BCI）领域，使用子带公共空间模式（SBCSP）和滤波器组公共空间模式（FBCSP）可以通过选择特定的频段来提高分类的准确性。但是，在跨学科分类中，由于不同学科之间存在个体差异，表现有限。

方法

本文介绍了迁移学习的思想，提出了子带目标对齐公共空间模式（SBTACSP）方法，并将其应用于运动意象（MI）脑电信号的跨学科分类。首先，将 EEG 信号带通滤波为多个频段（子带滤波）。随后，源域路径在每个频带中对齐到目标域空间。然后采用 CSP 算法提取特征，其中通过最小冗余最大相关性 (mRMR) 方法从每个子带中选择更具代表性的特征。然后融合所有子带的特征。最后，传统的线性判别分析 (LDA) 算法用于 MI 分类。

结果

我们的方法在 BCI 竞赛 Ⅳ 的数据集 Ⅱa 和 Ⅱb 上进行了评估。与六种最先进的算法相比，所提出的SBTACSP方法表现相对最好，在数据集Ⅱa和Ⅱb的跨学科分类中平均分类准确率分别达到75.15%和66.85%。

结论

因此，与任何一种相比，子带滤波和迁移学习的组合都实现了优越的分类性能。所提出的算法将极大地促进基于 MI 的 BCI 的实际应用。