Exploring dimensionality reduction of EEG features in motor imagery task classification
作者：
Highlights：
• This work analyzes feature selection and transformation methods for BCI systems.
• Three representative feature extraction methods are used: BP, Hjorth and AAR.
• An efficient LOO-CV technique is introduced for choosing the embedded dimensionality.
• Experiments have been conducted on five novice users during their first BCI sessions.
• According to its excellent results, LFDA is a promising method to design BCI systems.
摘要
•This work analyzes feature selection and transformation methods for BCI systems.•Three representative feature extraction methods are used: BP, Hjorth and AAR.•An efficient LOO-CV technique is introduced for choosing the embedded dimensionality.•Experiments have been conducted on five novice users during their first BCI sessions.•According to its excellent results, LFDA is a promising method to design BCI systems.
论文关键词：Brain-computer interfaces,Electroencephalogram signals,Motor imagery,Dimensionality reduction,Feature transformation,Linear discriminants,Local Fisher Discriminant Analysis
论文评审过程：Available online 6 March 2014.
论文官网地址：https://doi.org/10.1016/j.eswa.2014.02.043
