Motor imagery BCI classification based on multivariate variational mode decomposition

• Main Authors: Sadiq, Muhammad Tariq, Yu, Xiaojun, Yuan, Zhaohui, Aziz, Muhammad Zulkifal, Rehman, Naveed ur, Ding, Weiping, Xiao, Gaoxi
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Electrical and electronic engineering; Feature Extraction; Electroencephalography
• Online Access: https://hdl.handle.net/10356/162830
• Institution: Nanyang Technological University

In this article, a novel computer-aided diagnosis framework is proposed for the classification of motor imagery (MI) electroencephalogram (EEG) signals. First, a multivariate variational mode decomposition (MVMD) method was employed to obtain joint modes in frequency scale across all channels. Secon...