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

• المؤلفون الرئيسيون: Sadiq, Muhammad Tariq, Yu, Xiaojun, Yuan, Zhaohui, Zeming, Fan, Rehman, Ateeq Ur, Ullah, Inam, Li, Guoqi, Xiao, Gaoxi
• مؤلفون آخرون: School of Electrical and Electronic Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2021
• الموضوعات: Engineering::Electrical and electronic engineering; Electroencephalography; Multiscale Principal Component Analysis
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/145924
• المؤسسة: 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...