用于脑机接口的训练样本集扩增方法

Amplification Method of Training Sample Set for Brain Computer Interface

摘要: 在脑机接口技术研究中，为有效提高脑电信号分类正确率，提出了一种样本容量扩增方法。该方法将一组脑电数据的所有电极数据值增加一倍，然后与原始数据一起作为样本数据进行训练，这种方法不但增加了样本容量，而且进一步提高了训练的收敛速度及测试结果的可靠性。用贝叶斯线性判别法对脑机接口行列闪范式下公共数据扩增前后的样本进行训练和测试，在两种类别的数据中将数据在直线上进行投影。经研究发现投影后每一种样本扩增数据的投影点比原始数据的投影点更接近，两种类别的数据的类别中心点也更大，分类效果更好，平均正确率相比原始公共数据平均正确率有了显著性的提高。研究表明这种用于脑机接口的训练样本集扩增方法能显著提高脑电信号分类正确率。通过实例验证了所提出的样本扩增方法的有效性，为样本容量不足提供了可行的解决方法。

Abstract: In order to improve the classification accuracy of EEG signals, a method based on sample capacity amplification was proposed in this research of brain-computer interface technology. This method doubled all the electrode data value in one session of EEG data and then trained them as samples together with the original data, and this method not only increased the sample capacity, but also further improved the convergence speed of the train and the reliability of the test. After amplifying the public sample capacity, the new data and the original data were trained and tested by Bayesian Linear Discriminant Analysis method under the paradigm of row and column flash of brain-computer interface, then data were projected on a straight line in two categories of data. The results showed that the projected points of each kind of new data after projection were closer than the original public data, the center points of the two categories were larger and also the classification effect was better than the original public sample data and the average accuracy has been significantly improved compared with the average accuracy of the original public data. These results reflected that this amplification method of training sample set for brain computer interface can significantly improve the classification accuracy of EEG signals. An example was given to verify the validity of the proposed sample amplification method, which provided a feasible solution to the problem of insufficient sample capacity.