2016
DOI: 10.1038/srep30383
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Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients

Abstract: Brain-machine interfaces (BMIs) provide a new assistive strategy aimed at restoring mobility in severely paralyzed patients. Yet, no study in animals or in human subjects has indicated that long-term BMI training could induce any type of clinical recovery. Eight chronic (3–13 years) spinal cord injury (SCI) paraplegics were subjected to long-term training (12 months) with a multi-stage BMI-based gait neurorehabilitation paradigm aimed at restoring locomotion. This paradigm combined intense immersive virtual re… Show more

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Cited by 335 publications
(330 citation statements)
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References 84 publications
(104 reference statements)
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“…Chronic intracortical electrode signals degrade with time, which decreases BMI performance and success rates [9295]. To date, efforts to rescue performance have focused on designing new kinematic decoders [5659,63,65,96,97].…”
Section: Discussionmentioning
confidence: 99%
“…Chronic intracortical electrode signals degrade with time, which decreases BMI performance and success rates [9295]. To date, efforts to rescue performance have focused on designing new kinematic decoders [5659,63,65,96,97].…”
Section: Discussionmentioning
confidence: 99%
“…Researchers have been dedicated to improving the quality of life for these patients in several ways, e.g., (1) biological manipulation of the cellular milieu to encourage neuronal repair and regeneration (Magavi et al, 2000; Chen et al, 2002; Lee et al, 2004; Freund et al, 2006; Benowitz and Yin, 2007; Park et al, 2008; Maier et al, 2009; de Lima et al, 2012b; Dachir et al, 2014; Li et al, 2015; Omura et al, 2015), (2) creation of neural- or brain-machine interfaces designed to circumvent lesions and restore functionality (Wolpaw and McFarland, 1994; Kennedy and Bakay, 1998; Leuthardt et al, 2004; Monfils et al, 2004; Hochberg et al, 2006, 2012; Moritz et al, 2008; O'Doherty et al, 2009; Ethier et al, 2012; Collinger et al, 2013; Guggenmos et al, 2013; Ifft et al, 2013; Memberg et al, 2014; Zimmermann and Jackson, 2014; Grahn et al, 2015; Jarosiewicz et al, 2015; Soekadar et al, 2015; Bouton et al, 2016; Capogrosso et al, 2016; Donati et al, 2016; Hotson et al, 2016; Rajangam et al, 2016; Vansteensel et al, 2016), and (3) new rehabilitation techniques that include electrical stimulation and pharmacological enhancement of spinal circuitry to stimulate recovery (Carhart et al, 2004; Levy et al, 2008, 2016; Dy et al, 2010; Harkema et al, 2011, 2012; Dominici et al, 2012; van den Brand et al, 2012; Gad et al, 2013b, 2015; Angeli et al, 2014; Gharabaghi et al, 2014a,c; Wahl et al, 2014; Gerasimenko et al, 2015b). Unfortunately, the path to clinical relevance for these individual approaches remains long, and each field tends to operate largely in its own sphere of influence.…”
Section: Introductionmentioning
confidence: 99%
“…In these paradigms, either healthy subjects or patients were required to imagine movement of the arms to modulate EEG activity to generate motor commands to control the lower-limb movement. For instance, MI of hand movement was used to control the Lokomat (Hocoma AG, Volketswil, Switzerland), a commercially available robotic walker [9]. Another work by Lee et al used MI of both hands to build a cascaded ERD classifier, to control the Rex (Rex Bionics LTD, Auckland, New Zealand), a hand-free, self-supporting robotic mobility device [10].…”
Section: Introductionmentioning
confidence: 99%