Brain–Computer Interfaces in the Rehabilitation of Stroke and Neurotrauma

  • Surjo R. Soekadar
  • Niels Birbaumer
  • Leonardo G. Cohen


Paralysis after stroke or neurotrauma is among the leading causes of long term disability in adults. The development of brain–computer interface (BCI) systems that allow online classification of electric or metabolic brain activity and their translation into control signals of external devices or computers have led to two major approaches in tackling the problem of paralysis. While assistive BCI systems strive for continuous high-dimensional control of robotic devices or functional electric stimulation (FES) of paralyzed muscles to substitute for lost motor functions in a daily life environment (e.g. Velliste et al. 2008 [1]; Hochberg et al. 2006 [2]; Pfurtscheller et al. 2000 [3]), restorative BCI systems aim at normalization of ­neurophysiologic activity that might facilitate motor recovery (e.g. Birbaumer et al. 2007, 2009 [4, 5]; Daly et al. 2008 [6]). In order to make assistive BCI systems work in daily life, high BCI communication speed is necessary, an issue that by now can only be achieved by invasive recordings of brain activity (e.g. via multi-unit arrays, MUA, or electrocorticogram, ECoG). Restorative BCI systems, in contrast, were developed as training tools based on non-invasive methods such as electro- or magnetoencephalography (EEG/MEG). More recently developed approaches use real-time functional magnetic resonance imaging (rtfMRI) or near-infrared ­spectroscopy (NIRS). Here, we provide an overview of the current state in the development and application of assistive and restorative BCI and introduce novel approaches to improve BCI control with brain stimulation such as transcranial direct current stimulation (tDCS). The outlook of using BCI in rehabilitation of stroke and neurotrauma is discussed.


Transcranial Magnetic Stimulation Motor Imagery Functional Electric Stimulation Local Field Potential Anodal tDCS 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This contribution was supported by the NINDS intramural research program of the National Institutes of Health (NIH), the Deutsche Forschungsgemeinschaft (DFG) and the German Ministry of Education and Research (BMBF, 01GQ0831).


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Authors and Affiliations

  • Surjo R. Soekadar
    • 1
    • 2
  • Niels Birbaumer
    • 2
    • 3
  • Leonardo G. Cohen
    • 1
  1. 1.Human Cortical Physiology and Stroke Neurorehabilitation SectionNational Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH)BethesdaUSA
  2. 2.Institute of Medical Psychology and Behavioral NeurobiologyUniversity of TübingenTübingenGermany
  3. 3.Ospedale san Camillo, IRCCSVeniceItaly

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