A multi-class EEG-based BCI classification using multivariate empirical mode decomposition based filtering and Riemannian geometry
作者：
Highlights：
• A subject specific MEMD based filtering is proposed to classify MI EEG signals.
• The mean frequency is used to filter the MIMFs to obtain enhanced EEG signals.
• The sample covariance matrix feature is extracted from these enhanced EEG signals.
• This feature is classified using Riemannian geometry.
• Our combined approach outperformed other recognition methods.
摘要
•A subject specific MEMD based filtering is proposed to classify MI EEG signals.•The mean frequency is used to filter the MIMFs to obtain enhanced EEG signals.•The sample covariance matrix feature is extracted from these enhanced EEG signals.•This feature is classified using Riemannian geometry.•Our combined approach outperformed other recognition methods.
论文关键词：
论文评审过程：Received 20 June 2017, Revised 2 November 2017, Accepted 2 November 2017, Available online 7 November 2017, Version of Record 24 November 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.11.007
