A Framework of Adaptive Brain Computer Interfaces
A Framework of Adaptive Brain Computer Interfaces
复制标题
DOI：
10.1109/bmei.2009.5305646
复制
发表时间：
2009-10
期刊：
影响因子：
--
通讯作者：
Yan Li;Y. Koike;Masashi Sugiyama
中科院分区：
文献类型：
--
作者：
Yan Li;Y. Koike;Masashi Sugiyama
在脑计算机接口(BCI)的会话到会话传输中经常发现静态。针对这一问题，提出了一种基于公共空间模式(CSP)、线性判别分析(LDA)和协变量平移自适应方法的框架。协变量平移自适应是一种不需要标注测试会话数据就能适应测试会话的有效方法。将该框架应用于第三届脑机接口竞赛的一个皮层脑电数据集和一个脑电数据集上。尽管脑电和脑电的特征不同，但这两个数据集都表现出非平稳性。结果表明，与BCI竞赛中使用的方法相比，该框架具有更好的性能，说明了协变量移位自适应方法在处理脑机接口的非平稳性方面的有效性。
Stationarity is often found in session-to-session transfers of Brain Computer Interfaces (BCIs). To cope with the problem, a framework based on Common Spatial Patterns (CSP), Linear Discriminant Analysis (LDA), and covariate shift adaptation methods is proposed. Covariate shift adaptation is an effective method which can adapt to the testing sessions without the need for labeling the testing session data. This framework has been applied on one electrocorticogram (ECoG) dataset and one Electroencephalogram (EEG) dataset from BCI Competition III. Despite the different characteristics of ECoG and EEG, non-stationarity appeared in both datasets. Results showed that the proposed framework compares favorably with those methods used in the BCI Competition, revealing the effectiveness of covariate shift adaptation in tackling the nonstationarity in Brain Computer Interfaces.