Motor imagery EEG signals decoding by multivariate empirical wavelet transform-based framework for robust brain-computer interfaces

• Main Authors: Sadiq, Muhammad Tariq, Yu, Xiaojun, Yuan, Zhaohui, Zeming, Fan, Rehman, Ateeq Ur, Ullah, Inam, Li, Guoqi, Xiao, Gaoxi
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Electrical and electronic engineering; Electroencephalography; Multiscale Principal Component Analysis
• Online Access: https://hdl.handle.net/10356/145924
• Institution: Nanyang Technological University

The robustness and computational load are the key challenges in motor imagery (MI) based on electroencephalography (EEG) signals to decode for the development of practical brain-computer interface (BCI) systems. In this study, we propose a robust and simple automated multivariate empirical wavelet t...