Towards adaptive classification for BCI
Towards adaptive classification for BCI
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DOI：
10.1088/1741-2560/3/1/r02
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发表时间：
2006-03-01
影响因子：
4
通讯作者：
Mueller, Klaus-Robert
中科院分区：
文献类型：
--
作者：
Shenoy, Pradeep;Krauledat, Matthias;Mueller, Klaus-Robert
非平稳性在脑电信号中普遍存在。它们在使用基于脑电图的脑机接口（BCI）时尤其明显：(a)在初始校准测量和BCI在线操作之间的差异，或(b)由实验期间受试者大脑过程的变化引起的（例如由于疲劳，任务参与的变化等）。在本文中，我们首次量化了离线和在线会话期间记录的数据统计差异的系统证据。此外，我们提出了调查和可视化数据分布的新技术，这对于分析（非）平稳性特别有用。我们的研究表明，用于控制的大脑信号可以从离线校准会话到在线控制发生实质性变化，并且在单个会话中也是如此。除了信号的一般特征之外，我们还提出了几种自适应分类方案，并研究了它们在在线实验中记录的数据上的性能。我们研究的一个令人鼓舞的结果是，令人惊讶的简单自适应方法与离线特征选择方案相结合可以显着提高BCI性能。
Non-stationarities are ubiquitous in EEG signals. They are especially apparent in the use of EEG-based brain-computer interfaces (BCIs): (a) in the differences between the initial calibration measurement and the online operation of a BCI, or (b) caused by changes in the subject's brain processes during an experiment (e.g. due to fatigue, change of task involvement, etc). In this paper, we quantify for the first time such systematic evidence of statistical differences in data recorded during offline and online sessions. Furthermore, we propose novel techniques of investigating and visualizing data distributions, which are particularly useful for the analysis of (non-) stationarities. Our study shows that the brain signals used for control can change substantially from the offline calibration sessions to online control, and also within a single session. In addition to this general characterization of the signals, we propose several adaptive classification schemes and study their performance on data recorded during online experiments. An encouraging result of our study is that surprisingly simple adaptive methods in combination with an offline feature selection scheme can significantly increase BCI performance.