Machine Learning Group, Berlin Institute of Technology, Franklinstr. 28/29, 10587 Berlin, Germany.
Neural Netw. 2009 Nov;22(9):1313-9. doi: 10.1016/j.neunet.2009.07.020. Epub 2009 Jul 22.
Several feature types have been used with EEG-based Brain-Computer Interfaces. Among the most popular are logarithmic band power estimates with more or less subject-specific optimization of the frequency bands. In this paper we introduce a feature called Time Domain Parameter that is defined by the generalization of the Hjorth parameters. Time Domain Parameters are studied under two different conditions. The first setting is defined when no data from a subject is available. In this condition our results show that Time Domain Parameters outperform all band power features tested with all spatial filters applied. The second setting is the transition from calibration (no feedback) to feedback, in which the frequency content of the signals can change for some subjects. We compare Time Domain Parameters with logarithmic band power in subject-specific bands and show that these features are advantageous in this situation as well.
基于脑电图的脑机接口已经使用了多种特征类型。其中最受欢迎的是对数频带功率估计，或多或少地对频带进行了特定于主体的优化。在本文中，我们引入了一种称为时域参数的特征，它是由 Hjorth 参数的泛化定义的。时域参数在两种不同的情况下进行研究。第一种情况是在没有主体数据的情况下定义的。在这种情况下，我们的结果表明，时域参数在所有应用了所有空间滤波器的频带功率特征中表现优于其他所有特征。第二种情况是从校准（无反馈）到反馈的过渡，在这种情况下，信号的频率内容可能会发生变化。我们将时域参数与特定于主体的频带中的对数频带功率进行了比较，并表明在这种情况下这些特征也具有优势。
