位置：成果数据库 > 期刊 > 期刊详情页
The Extreme Energy Ratio Criterion for EEG Feature Extraction
  • 期刊名称：Lecture Notes in Computer Science
  • 时间：0
  • 页码：919-928
  • 语言：英文
  • 相关项目：脑机接口中脑电信号特征提取与模式分类的理论与方法研究
作者： 孙仕亮|
同期刊论文项目
脑机接口中脑电信号特征提取与模式分类的理论与方法研究
期刊论文 11 会议论文 10
同项目期刊论文
Extreme Energy Difference for Feature Extraction of EEG Signals
Local Within-Class Accuracies for Weighting Individual Outputs in Multiple Classifier Systems
Active Learning with Extremely Sparse Labeled Examples
Multiple-View Multiple-Learner Active Learning
Importance Weighted Extreme Energy Ratio for EEG Classification
A Theoretical Derivation of the Kernel Extreme Energy Ratio Method for EEG Feature Extraction
The Random Electrode Selection Ensemble for EEG Signal Classification
A Multitask Learning Approach to Face Recognition Based on Neural Networks
Hierarchical Multi-view Fisher Discriminant Analysis
A Theory of Kernel Extreme Energy Difference for Feature Extraction of EEG Signals