Advanced signal processing and modeling for neuroengineering
Signal processing and modeling techniques have been consistently playing a significant role in the field of neuroengineering research. This special issue will focus on the use and elaboration of latest signal processing and modeling techniques, e.g., deep machine learning, nonlinear dynamical approaches, etc., to analyze biomedical data relevant for neuroengineering research.
More specifically, these advanced techniques are applied to EMG, EEG, brain-computer and brain-machine interfaces, neural computation and modeling, neural prostheses, neuro-robotics, neuromodulation, etc. The special issue will be an international forum for researchers working in the fields of neuroengineering, computational neuroscience, and integrative physiology to report the most recent developments and ideas, especially in their clinical applications. This Special Issue emphasizes (but not limited to) the following research topics:
• Noise suppression and removal in analyzing neurophysiological signals
• Nonlinear dynamical approaches and multivariate and multiscale techniques for analyzing neurophysiological signals
• Application of machine learning and deep neural networks for detection and classification of and neurological diseases
• Advanced signal processing in brain-computer interface and neuro-prosthetic devices
• Acquisition and analysis of neurophysiological signals from mobile and wearable devices and body sensor network techniques
• Clinical applications of advanced signal processing in neuroengineering
The Special Issue is Guest Edited by Fei Chen, Shi-xiong Chen and Dong-mei Hao.