Korczowski L, Congedo M, Jutten C
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:1769-72. doi: 10.1109/EMBC.2015.7318721.
The classification of electroencephalographic (EEG) data recorded from multiple users simultaneously is an important challenge in the field of Brain-Computer Interface (BCI). In this paper we compare different approaches for classification of single-trials Event-Related Potential (ERP) on two subjects playing a collaborative BCI game. The minimum distance to mean (MDM) classifier in a Riemannian framework is extended to use the diversity of the inter-subjects spatio-temporal statistics (MDM-hyper) or to merge multiple classifiers (MDM-multi). We show that both these classifiers outperform significantly the mean performance of the two users and analogous classifiers based on the step-wise linear discriminant analysis. More importantly, the MDM-multi outperforms the performance of the best player within the pair.
同时对多个用户记录的脑电图（EEG）数据进行分类是脑机接口（BCI）领域的一项重要挑战。在本文中，我们比较了在两个玩协作式BCI游戏的受试者上对单次试验事件相关电位（ERP）进行分类的不同方法。黎曼框架下的最小距离均值（MDM）分类器被扩展，以利用受试者间时空统计的多样性（MDM-hyper）或合并多个分类器（MDM-multi）。我们表明，这两种分类器的性能均显著优于两个用户的平均性能以及基于逐步线性判别分析的类似分类器。更重要的是，MDM-multi的性能优于该对中最佳玩家的性能。
