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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.

  1. Rehabilitation robots can provide intensive physical training after stroke. However, variations of the rehabilitation effects in translation from well-controlled research studies to clinical services have not ...

    Authors: Yanhuan Huang, Will Poyan Lai, Qiuyang Qian, Xiaoling Hu, Eric W. C. Tam and Yongping Zheng
    Citation: BioMedical Engineering OnLine 2018 17:91
  2. Intra-body communication (IBC) is one of the highlights in studies of body area networks. The existing IBC studies mainly focus on human channel characteristics of the physical layer, transceiver design for th...

    Authors: Yue-Ming Gao, Heng-fei Zhang, Shi Lin, Rui-Xin Jiang, Zhi-Ying Chen, Željka Lučev Vasić, Mang-I Vai, Min Du, Mario Cifrek and Sio-Hang Pun
    Citation: BioMedical Engineering OnLine 2018 17:71
  3. In longitudinal electroencephalography (EEG) studies, repeatable electrode positioning is essential for reliable EEG assessment. Conventional methods use anatomical landmarks as fiducial locations for the elec...

    Authors: Chanho Song, Sangseo Jeon, Seongpung Lee, Ho-Gun Ha, Jonghyun Kim and Jaesung Hong
    Citation: BioMedical Engineering OnLine 2018 17:64
  4. The electroencephalogram (EEG) signal represents a subject’s specific brain activity patterns and is considered as an ideal biometric given its superior invisibility, non-clonality, and non-coercion. In order ...

    Authors: Qunjian Wu, Bin Yan, Ying Zeng, Chi Zhang and Li Tong
    Citation: BioMedical Engineering OnLine 2018 17:55