title: Decoding upper limb residual muscle activity in severe chronic stroke.
abstract: OBJECTIVE
  Stroke is a leading cause of long-term motor disability. Stroke patients with severe hand weakness do not profit from rehabilitative treatments. Recently, brain-controlled robotics and sequential functional electrical stimulation allowed some improvement. However, for such therapies to succeed, it is required to decode patients' intentions for different arm movements. Here, we evaluated whether residual muscle activity could be used to predict movements from paralyzed joints in severely impaired chronic stroke patients.
  
  
  METHODS
  Muscle activity was recorded with surface-electromyography (EMG) in 41 patients, with severe hand weakness (Fugl-Meyer Assessment [FMA] hand subscores of 2.93 ± 2.7), in order to decode their intention to perform six different motions of the affected arm, required for voluntary muscle activity and to control neuroprostheses. Decoding of paretic and nonparetic muscle activity was performed using a feed-forward neural network classifier. The contribution of each muscle to the intended movement was determined.
  
  
  RESULTS
  Decoding of up to six arm movements was accurate (>65%) in more than 97% of nonparetic and 46% of paretic muscles.
  
  
  CONCLUSIONS
  These results demonstrate that some level of neuronal innervation to the paretic muscle remains preserved and can be used to implement neurorehabilitative treatments in 46% of patients with severe paralysis and extensive cortical and/or subcortical lesions. Such decoding may allow these patients for the first time after stroke to control different motions of arm prostheses through muscle-triggered rehabilitative treatments.
authors: Institute of Medical Psychology and Behavioral Neurobiology and MEG Center, University of Tübingen Silcherstraße 5, 72076, Tübingen, Germany ; TECNALIA Mikeletegi Pasalekua 1, 20009, San Sebastian, Spain.
authors: Ander
authors: A
authors: Ramos-Murguialday
authors: Institute of Medical Psychology and Behavioral Neurobiology and MEG Center, University of Tübingen Silcherstraße 5, 72076, Tübingen, Germany.
authors: Eliana
authors: E
authors: García-Cossio
authors: Department of Computer Engineering, Wilhelm-Schickard-Institute, University of Tübingen Sand 14, 72076, Tübingen, Germany.
authors: Armin
authors: A
authors: Walter
authors: Institute of Medical Psychology and Behavioral Neurobiology and MEG Center, University of Tübingen Silcherstraße 5, 72076, Tübingen, Germany ; Daegu Gyeongbuk Institute of Science and Technology (DGIST) 333, Techno jungang-daero, Hyeonpung-myeon, Dalseong-gun, 711-873, Daegu, Korea.
authors: Woosang
authors: W
authors: Cho
authors: Institute of Medical Psychology and Behavioral Neurobiology and MEG Center, University of Tübingen Silcherstraße 5, 72076, Tübingen, Germany.
authors: Doris
authors: D
authors: Broetz
authors: Department of Computer Engineering, Wilhelm-Schickard-Institute, University of Tübingen Sand 14, 72076, Tübingen, Germany ; Department of Computer Engineering, University of Leipzig Augustusplatz 10, 04109, Leipzig, Germany.
authors: Martin
authors: M
authors: Bogdan
authors: Human Cortical Physiology and Neurorehabilitation Section, National Institute of Neurological Disorders and Stroke, National Institute of Health 10 Center Drive, 20892, Bethesda, Maryland.
authors: Leonardo G
authors: LG
authors: Cohen
authors: Institute of Medical Psychology and Behavioral Neurobiology and MEG Center, University of Tübingen Silcherstraße 5, 72076, Tübingen, Germany ; Ospedale San Camillo, Istituto di Ricovero e Cura a Carattere Scientifico Via Alberoni, 70, 30126, Venezia, Italy ; German Center for Diabetes Research (DZD) Tubingen, Germany.
authors: Niels
authors: N
authors: Birbaumer
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