基于脑电的脑-机接口:关键技术和应用前景

Electroencephalogram Based Brain-Computer Interface: Key Techniques and Application Prospect

介绍了运动想像脑-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。

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.