Towards Efficient Decoding of Multiple Classes of Motor Imagery Limb Movements Based on EEG Spectral and Time Domain Descriptors

• Published in: Journal of medical systems Vol. 41; no. 12; pp. 194 - 13
• Main Authors: Samuel, Oluwarotimi Williams, Geng, Yanjuan, Li, Xiangxin, Li, Guanglin
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2017; Springer Nature B.V
• Subjects: Accuracy; Amputation; Brain; Convergence of Deep Machine Learning and Nature Inspired Computing Paradigms for Medical Informatics; Decoding; EEG; Electroencephalography; Electromyography; Ethics; Feature extraction; Health Informatics; Health Sciences; Identification methods; Image & Signal Processing; Imagery; Medicine; Medicine & Public Health; Mental task performance; Pattern recognition; Prostheses; Prosthetics; Statistics for Life Sciences; Time domain analysis; Brain-computer-interface; Electromyography; Electroencephalography; Motor imagery; Pattern recognition; Upper limb prostheses

To control multiple degrees of freedom (MDoF) upper limb prostheses, pattern recognition (PR) of electromyogram (EMG) signals has been successfully applied. This technique requires amputees to provide sufficient EMG signals to decode their limb movement intentions (LMIs). However, amputees with neur...