The brain-computer interface (BCI) connects the brain and the external world through an information transmission channel by interpreting the physiological information of the brain during thinking activities. The effective classification of electroencephalogram (EEG) signals is the key to improving the performance of the system. To improve the classification accuracy of EEG signals in the BCI system, the transfer learning algorithm and the improved Common Spatial Pattern (CSP) algorithm are combined to construct a data classification model. Finally, the effectiveness of the proposed algorithm is verified. The results show that in actual and imagined movements, the accuracy of the left- and right-hand movements at different speeds is higher than when the speeds are the same. The proposed Adaptive Composite Common Spatial Pattern (ACCSP) and Self Adaptive Common Spatial Pattern (SACSP) algorithms have good classification effects on 5 subjects, with an average classification accuracy rate of 83.58 percent, which is an increase of 6.96 percent compared with traditional algorithms. When the training sample size is 10, the classification accuracy of the ACCSP algorithm is higher than that of the traditional CSP algorithm. The improved CSP algorithm combined with transfer learning embodies a good classification effect in both ACCSP and SACSP. Especially, the performance of SACSP mode is better. Combining the improved CSP algorithm proposed with the CSP-based transfer learning algorithm can improve the classification accuracy of the BCI classifier.

中文翻译：

---

用于脑机接口的高级机器学习方法

脑机接口（BCI）通过在思维活动中解释大脑的生理信息，通过信息传输通道连接大脑和外部世界。脑电图（EEG）信号的有效分类是提高系统性能的关键。为了提高脑机接口系统中脑电信号的分类精度，将迁移学习算法和改进的通用空间模式（CSP）算法相结合，构建数据分类模型。最后，验证了所提算法的有效性。结果表明，在实际动作和想象动作中，不同速度下左右手动作的准确度要高于速度相同时。所提出的自适应复合公共空间模式（ACCSP）和自自适应公共空间模式（SACSP）算法对5个主题的分类效果良好，平均分类准确率为83.58%，与传统算法相比提高了6.96个百分点。当训练样本量为 10 时，ACCSP 算法的分类准确率高于传统 CSP 算法。改进后的CSP算法结合迁移学习在ACCSP和SACSP中都体现了良好的分类效果。尤其是 SACSP 模式的性能更好。将提出的改进CSP算法与基于CSP的迁移学习算法相结合，可以提高BCI分类器的分类精度。