adaptive signal processing; brain-computer interfaces; electroencephalography; electronic data interchange; feature extraction; learning (artificial intelligence); medical signal processing; neurophysiology; signal classification; statistical analysis; BCI competition; BCI performance deterioration; EEG based brain-computer interface; adaptive BCI system; brain activity; classification accuracy; covariate shift-adaptation; electrode placement; electroencephalogram data; impedance variation; input data distribution shift; intersession transfer; intrasession transfer; nonstationarity handling; nonstationary EEG signal characteristics; robust BCI system; statistical properties; testing phase; traditional learning method; training phase; transductive learning model; Brain modeling; Data models; Electroencephalography; Feature extraction; Filtering; Testing; Training; Non-stationary learning; covaraite shift adaptation; semi-supervised learning; transductive learning;
机译:基于协变量偏移估计的自适应集成学习用于处理与运动图像相关的基于脑电图的脑机接口中的非平稳性
机译:基于协变速估计的自适应集合学习,用于处理电动机图像相关EEG基础脑电电脑界面的非实用性
机译:基于主成分的协变量移位自适应以减少基于MEG的脑机接口的非平稳性
机译:学习自适应主题独立的P300模型,用于基于EEG的脑电电脑界面
机译:解决基于EEG的脑机接口在信号质量和校准时间方面的挑战
机译:基于脑电电脑接口的EEG解码转移学习的应用:综述
机译:基于主成分的协变量移位自适应以减少基于MEG的脑机接口的非平稳性