Background

The classification of psychological tasks such as motor imagery based on electroencephalography (EEG) signals is an essential issue in the brain computer interface (BCI) system. The feature extraction is an important issue for improving classification accuracy of BCI system.

New Method

For extracting discriminative features, common spatial pattern (CSP) is an effective feature extraction method. However, features extracted by CSP are dense, and even feature patterns are repeatedly selected in the feature space. A sparse CSP algorithm is proposed, which embeds the sparse techniques and iterative search into the CSP. To improve the classification performance, two regularization parameters are added to the traditional linear discriminant analysis (LDA).

Results

The sparse CSP algorithm can select several channels of EEG signals with the most obvious features. The improved regularized discriminant analysis is used to solve the singularity problem and improve the feature classification accuracy.

Comparison with Existing Method(s): The proposed algorithm was evaluated by the data set I of the IVth BCI competition and our dataset. The experimental results of the BCI competition dataset show that accuracy of the improved algorithm is 10.75 % higher than that of the traditional algorithm. Comparing with the currently existing methods for the same data, it also shows excellent classification performance. The effectiveness of the improved algorithm is also shown in experiments on our dataset.

Conclusions

It sufficiently proves that the improved algorithm proposed in this paper improves the classification performance of motor intent recognition.

中文翻译：

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基于稀疏的常见空间格局和正则判别分析，改进运动图像的脑电分类。

背景

心理任务的分类，例如基于脑电图（EEG）信号的运动图像，是脑计算机接口（BCI）系统中的重要问题。特征提取是提高BCI系统分类精度的重要问题。

新方法

对于提取歧视性特征，公共空间模式（CSP）是一种有效的特征提取方法。但是，CSP提取的特征很密集，甚至在特征空间中也反复选择了特征模式。提出了一种稀疏CSP算法，该算法将稀疏技术和迭代搜索嵌入到CSP中。为了提高分类性能，将两个正则化参数添加到传统的线性判别分析（LDA）中。

结果

稀疏的CSP算法可以选择具有最明显特征的多个EEG信号通道。改进的正则判别分析用于解决奇异性问题，提高特征分类的准确性。

与现有方法的比较：通过第四届BCI竞赛的数据集I和我们的数据集对提出的算法进行了评估。BCI竞争数据集的实验结果表明，改进算法的准确性比传统算法高10.75％。与当前针对相同数据的方法相比，它还显示了出色的分类性能。在我们的数据集上的实验中也显示了改进算法的有效性。

结论

充分证明了本文提出的改进算法提高了运动意图识别的分类性能。