The College of Automation Science and Engineering, South China University of Technology, 510640 Guangzhou, China.
Cogn Neurodyn. 2010 Sep;4(3):207-16. doi: 10.1007/s11571-010-9114-0. Epub 2010 Jun 8.
Parameter setting plays an important role for improving the performance of a brain computer interface (BCI). Currently, parameters (e.g. channels and frequency band) are often manually selected. It is time-consuming and not easy to obtain an optimal combination of parameters for a BCI. In this paper, motor imagery-based BCIs are considered, in which channels and frequency band are key parameters. First, a semi-supervised support vector machine algorithm is proposed for automatically selecting a set of channels with given frequency band. Next, this algorithm is extended for joint channel-frequency selection. In this approach, both training data with labels and test data without labels are used for training a classifier. Hence it can be used in small training data case. Finally, our algorithms are applied to a BCI competition data set. Our data analysis results show that these algorithms are effective for selection of frequency band and channels when the training data set is small.
参数设置对于提高脑机接口 (BCI) 的性能起着重要作用。目前，参数（例如通道和频带）通常是手动选择的。对于 BCI 来说，找到参数的最佳组合既耗时又不容易。本文考虑了基于运动想象的 BCI，其中通道和频带是关键参数。首先，提出了一种半监督支持向量机算法，用于自动选择给定频带的一组通道。接下来，将该算法扩展用于联合通道-频带选择。在这种方法中，使用带标签的训练数据和不带标签的测试数据来训练分类器。因此，它可以用于小训练数据集的情况。最后，我们的算法应用于 BCI 竞赛数据集。数据分析结果表明，当训练数据集较小时，这些算法对于频带和通道的选择是有效的。
