Machine Learning Laboratory, Berlin Institute of Technology, Franklinstrasse 28/29, 10587, Berlin, Germany.
Brain Topogr. 2010 Jun;23(2):186-93. doi: 10.1007/s10548-010-0135-0. Epub 2010 Feb 17.
One crucial question in the design of electroencephalogram (EEG)-based brain-computer interface (BCI) experiments is the selection of EEG channels. While a setup with few channels is more convenient and requires less preparation time, a dense placement of electrodes provides more detailed information and henceforth could lead to a better classification performance. Here, we investigate this question for a specific setting: a BCI that uses the popular CSP algorithm in order to classify voluntary modulations of sensorimotor rhythms (SMR). In a first approach 13 different fixed channel configurations are compared to the full one consisting of 119 channels. The configuration with 48 channels results to be the best one, while configurations with less channels, from 32 to 8, performed not significantly worse than the best configuration in cases where only few training trials are available. In a second approach an optimal channel configuration is obtained by an iterative procedure in the spirit of stepwise variable selection with nonparametric multiple comparisons. As a surprising result, in the second approach a setting with 22 channels centered over the motor areas was selected. Thanks to the acquisition of a large data set recorded from 80 novice participants using 119 EEG channels, the results of this study can be expected to have a high degree of generalizability.
在基于脑电图（EEG）的脑机接口（BCI）实验的设计中，一个关键问题是 EEG 通道的选择。虽然使用少量通道的设置更方便，且所需的准备时间更少，但密集放置电极可以提供更详细的信息，从而可以提高分类性能。在这里，我们针对特定设置研究了这个问题：一个使用流行的 CSP 算法来对感觉运动节律（SMR）的自愿调制进行分类的 BCI。在第一种方法中，将 13 种不同的固定通道配置与包含 119 个通道的全通道配置进行了比较。结果表明，包含 48 个通道的配置是最好的，而在训练次数较少的情况下，从 32 到 8 的通道配置与最佳配置的性能相差不大。在第二种方法中，通过基于逐步变量选择的非参数多重比较的迭代过程来获得最佳通道配置。一个令人惊讶的结果是，在第二种方法中，选择了一个以运动区域为中心的 22 通道设置。由于从 80 名新手参与者那里采集了使用 119 个 EEG 通道记录的大量数据集，预计这项研究的结果具有很高的通用性。
