Comparison of adaptive features with linear discriminant classifier for Brain computer Interfaces
Comparison of adaptive features with linear discriminant classifier for Brain computer Interfaces
复制标题
脑机接口自适应特征与线性判别分类器的比较
DOI：
10.1109/iembs.2008.4649118
复制
发表时间：
2008
期刊：
影响因子：
--
通讯作者：
A. Schlogl
中科院分区：
文献类型：
--
作者：
C. Vidaurre;A. Schlogl
许多脑机接口（BCI）使用频带功率估计，或多或少地对特定学科的频带进行优化。然而，一些备选的EEG特征不需要选择频带；对这些特性的估计器进行了修改，以适应使用。将流行的频带功率估计与自适应自回归参数、Hjorth、Barlow、Wackermann、Brain-Rate和一种新的特征类型Time Domain Parameter进行了比较。来自21个科目的结果表明，在不需要针对特定科目进行优化的情况下，有几个特征提供了同样好的甚至更好的性能，并且在不知道科目的最区分频带时，它们也优于频带功率。
Many Brain-computer Interfaces (BCI) use band-power estimates with more or less subject-specific optimization of the frequency bands. However, a number of alternative EEG features do not need to select the frequency bands; estimators for these features have been modified for an adaptive use. The popular band power estimates were compared with Adaptive AutoRegressive parameters, Hjorth, Barlow, Wackermann, Brain-Rate and a new feature type called Time Domain Parameter. The results from 21 subjects show that several features provide an equally good or even better performance, while no subject-specific optimization is needed, and they are also preferable to band power when the most discriminating frequency band of a subject is not known.
影响因子：
14.9
作者：
Blankertz, Benjamin;Tomioka, Ryota;Mueller, Klaus-Robert
通讯作者：
Mueller, Klaus-Robert