Reconstructing four joint angles on the shoulder and elbow from noninvasive electroencephalographic signals through electromyography

• Main Author: Kyuwan eChoi
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2013-10-01
• Series: Frontiers in Neuroscience
• Subjects: EEG; primary motor cortex; neural activity; neuroprosthetics; EMG
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fnins.2013.00190/full

In this study, first the cortical activities over 2240 vertexes on the brain were estimated from 64 channels electroencephalography (EEG) signals using the Hierarchical Bayesian estimation while 5 subjects did continuous arm reaching movements. From the estimated cortical activities, a sparse linear regression method selected only useful features in reconstructing the electromyography (EMG) signals and estimated the EMG signals of 9 arm muscles. Then, a modular artificial neural network was used to estimate four joint angles from the estimated EMG signals of 9 muscles: one for movement control and the other for posture control. The estimated joint angles using this method have the correlation coefficient of 0.807 (±0.10) and the normalized root-mean-square error (nRMSE) of 0.176 (±0.29) with the actual joint angles.