Subscribe to RSS
DOI: 10.1055/s-0033-1357209
Gehirn-Maschine-Interfaces (Brain-Machine Interfaces, BMI) zur Rehabilitation von Schlaganfall
BMI-Training with Body-Internalized FES or Other Wireless and/or Portable DevicesPublication History
Publication Date:
19 December 2013 (online)
Zusammenfassung
BMI übersetzt Hirnsignale in Signale für körper-externe Maschinen und Computer ohne Beteiligung des motorischen Systems. BMIs wurden zur Rehabilitation von chronischem Schlaganfall meist in Kombination mit funktioneller Elektrostimulation (FES), Robotern und Neuroprothesen und Physiotherapie benutzt. Zusätzlich zeigt Neurofeedback- und Biofeedbacktraining vielversprechende Ergebnisse als zusätzliche Rehabilitationsstrategie. Wenig gut kontrollierte klinische Studien mit hinreichend großen und homogenen Patientenstichproben stehen zur Verfügung, die meisten sind „proof-of-principle“ Versuche mit kleinen Stichproben. Die Kombination aus BMI-Neuroprothesen-Training mit Verhaltens-orientierter Physiotherapie hat sich als die wirksamste nicht-invasive Strategie bei schwerst gelähmten chronischen Schlaganfallpatienten erwiesen. In der Zukunft sollten invasive BMI-Trainings mit internalisierter FES und anderen drahtlosen und tragbaren Prothesen geprüft werden.
Abstract
BMI translates a brain signal to an external device without any motor involvement. BMIs have been used for rehabilitation of chronic stroke in combination with output devices such as functional electrical stimulation (FES), robots and exosceletons as a neuroprosthetic device, and physiotherapy. In addition, neurofeedback and biofeedback (usually using electromyographic (EMG) feedback) shows great promise as a rehabilitation strategy. However, very few adequately controlled studies with large enough patient samples are available, most report proof-of-principle strategies. The combination of BMI with behaviorally oriented physiotherapy to generalize BMI-treatment effects to the home environment proved to be the most efficient non-invasive rehabilitation strategy for severely paralyzed stroke victims. Future directions should test invasive.
-
Literatur
- 1 Birbaumer N, Elbert T, Canavan A et al. Slow Potentials of the Cerebral Cortex and Behavior. Physiological Reviews 1990; 70: 1-41
- 2 Birbaumer N, Cohen L. Brain-Computer-Interfaces (BCI): Communication and Restoration of Movement in Paralysis. The Journal of Physiology. 2007; 579 (Pt3) 621-636
- 3 Birbaumer N, Ruiz S, Sitaram R. Learned regulation of brain metabolism. Trends in Cognitive Sciences, TICS- 2013; 1197
- 4 Buch E, Weber C, Cohen LG et al. Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. 2008; Stroke 39: 910-917
- 5 Buch ER, Shanechi AM, Fourkas AD et al. Parietofrontal integrity determines neural modulation associated with grasping imagery after stroke. Brain 2012; 135: 596-614
- 6 Collinger JL, Wodlinger B, Downey JE et al. High-performance neuroprosthetic control by an individual with tetraplegia. The Lancet 2013; 381: 557-564
- 7 Dobkin BH. Brain-computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation. J Physiol 2007; 579: 637-642
- 8 Fetz EE. Operant conditioning of cortical unit activity. Science 1969; 163: 955-958
- 9 Ganguly K, Secundo L, Ranade G et al. Cortical representation of ipsilateral arm movements in monkey and man. J Neurosci 2009; 29: 12948-12956
- 10 Hochberg LR, Serruya MD, Friehs GM et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 2006; 442: 164-171
- 11 Hochberg LR, Bacher D, Jarosiewicz B et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 2012; 485: 372-375
- 12 Koralek AC, Jin X, Long JD et al. Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills. Nature 2012; 483: 331-335
- 13 Nishimura Y, Perlmutter SI, Fetz EE. Restoration of upper limb movement via artificial corticospinal and musculospinal connections in a monkey with spinal cord injury. Front Neural Circuits 2013; 7: 57 DOI: 10.3389/fncir.2013.00057.
- 14 Ramos-Murguialday A, Schürholz M, Caggiano V et al. Proprioceptive feedback and brain computer interface (BCI) based neuroprostheses. PloS ONE 2012; 7: e47048 DOI: 10.1371.
- 15 Ramos-Murguialday A, Broetz D, Rea M et al. Brain-machine-interface in chronic stroke rehabilitation: a controlled study. Annals of Neurology 2013; DOI: 10.1002/ana.23879.
- 16 Shibata K, Watanabe T, Takeo S et al. Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation. Science 2011; 334: 1413-1415
- 17 Silvoni S, Ramos-Murguialday A, Cavinato M et al. Brain-Computer-interface in stroke: a review of progress. Clinical EEG and Neuroscience 2011; 42: 245-252
- 18 Silvoni S, Cavinato M, Volpato C et al. (in press). An assisted-force-feedback brain-machine interface for motor-rehabilitation. Frontiers in Neuroprosthetics
- 19 Silvoni S, Cavinato M, Volpato C et al. Amyotrophic Lateral Sclerosis progression and stability of brain-computer interface communication. Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, early online 2013; 1-7
- 20 Waldert S, Preissl H, Demandt E et al. Hand movement direction decoded from MEG and EEG. J Neurosci 2008; 28: 1000-1008