title: Neural coding for effective rehabilitation.
abstract: Successful neurological rehabilitation depends on accurate diagnosis, effective treatment, and quantitative evaluation. Neural coding, a technology for interpretation of functional and structural information of the nervous system, has contributed to the advancements in neuroimaging, brain-machine interface (BMI), and design of training devices for rehabilitation purposes. In this review, we summarized the latest breakthroughs in neuroimaging from microscale to macroscale levels with potential diagnostic applications for rehabilitation. We also reviewed the achievements in electrocorticography (ECoG) coding with both animal models and human beings for BMI design, electromyography (EMG) interpretation for interaction with external robotic systems, and robot-assisted quantitative evaluation on the progress of rehabilitation programs. Future rehabilitation would be more home-based, automatic, and self-served by patients. Further investigations and breakthroughs are mainly needed in aspects of improving the computational efficiency in neuroimaging and multichannel ECoG by selection of localized neuroinformatics, validation of the effectiveness in BMI guided rehabilitation programs, and simplification of the system operation in training devices.
authors.affiliation: Interdisciplinary Division of Biomedical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
authors.forename: Xiaoling
authors.identifier: 0000-0003-3188-3005
authors.initials: X
authors.lastname: Hu
authors.affiliation: Qiushi Academy for Advanced Studies, Zhejiang University, Zhejiang 310027, China.
authors.forename: Yiwen
authors.identifier: 0000-0002-1878-6182
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Howard Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, VA 20147, USA.
authors.forename: Ting
authors.identifier: 0000-0002-8053-412X
authors.initials: T
authors.lastname: Zhao
authors.affiliation: J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA.
authors.forename: Aysegul
authors.identifier: 0000-0001-7925-0747
authors.initials: A
authors.lastname: Gunduz
mesh_terms: D001921:Brain / Q000502:physiology*; D004576:Electromyography; D006801:Humans; D009422:Nervous System Diseases / Q000503:physiopathology / Q000534:rehabilitation*; D059906:Neuroimaging; D012371:Robotics; D014584:User-Computer Interface