欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
介绍了运动想像脑一机接口技术中的几项核心技术，即在信息特征提取阶段采用的共空间模式和判决空间模式滤波方法、在模式识别阶段采用的大概率测试样本扩充训练集合的贝叶斯线性判决分析方法、直推式支持向量机方法、基于流形学习的拉普拉斯支持向量机方法和基于分层贝叶斯模型的方法。介绍了在线系统设计中的放大器设计和空闲态检测，展望了未来的发展方向。
英文摘要：
Several key techniques of brain computer-interface based on motor imagery are introduced. For the feature extraction, emphasized are the common spatial patterns （CSP） and discriminative spatial patterns （DSP） filters; for the pattern recognition, stressed are the Bayesian linear discriminant analysis （BLDA） employed large probabilistic test samples to expand the training set, the transductive support vector machines （TSVM）, the manifold-based Laplacian support vector machine （LapSvm）, and the hierarchical Bayesian linear discriminant analysis. For on-line system realization, amplifier designing and the idle-state detection are described. Finally, the potential future directions are discussed.
同期刊论文项目
同项目期刊论文
Long-range correlations of different EEG derivations in rats: sleep stage-dependent generators may p
Partial information can be transmitted in auditory channel: Inferences from lateralized readiness po
Electrophysiological Explorations of the Cause and Effect of Inhibition of Return in a Cue-Target Pa
MMN evidence for asymmetry in detection of IOI shortening and lengthening at behavioral indifference
Altered Functional Connectivity in Default Mode Network in Absence Epilepsy: A Resting-State fMRI St
Contour Detection Based on a Non-classical Receptive Field Model with Butterfly-shaped Inhibition Su
A study on the neural mechanism of inhibition of return by the event-related potential in the Go/Nog
期刊信息
位置：