Abstract
Brain–computer interfaces (BCI) use neuroelectric and metabolic brain activity to activate peripheral devices and computers without mediation of the motor system. In order to activate the BCI patients have to learn a certain amount of brain control. Self-regulation of brain activity was found to follow the principles of skill learning and instrumental conditioning. This review focuses on the clinical application of brain–computer interfaces in paralyzed patients with locked-in syndrome and completely locked-in syndrome (CLIS). It was shown that electroencephalogram (EEG)-based brain–computer interfaces allow selection of letters and words in a computer menu with different types of EEG signals. However, in patients with CLIS without any muscular control, particularly of eye movements, classical EEG-based brain–computer interfaces were not successful. Even after implantation of electrodes in the human brain, CLIS patients were unable to communicate. We developed a theoretical model explaining this fundamental deficit in instrumental learning of brain control and voluntary communication: patients in complete paralysis extinguish goal-directed response-oriented thinking and intentions. Therefore, a reflexive classical conditioning procedure was developed and metabolic brain signals measured with near infrared spectroscopy were used in CLIS patients to answer simple questions with a “yes” or “no”-brain response. The data collected so far are promising and show that for the first time CLIS patients communicate with such a BCI system using metabolic brain signals and simple reflexive learning tasks. Finally, brain machine interfaces and rehabilitation in chronic stroke are described demonstrating in chronic stroke patients without any residual upper limb movement a surprising recovery of motor function on the motor level as well as on the brain level. After extensive combined BCI training with behaviorally oriented physiotherapy, significant improvement in motor function was shown in this previously intractable paralysis. In conclusion, clinical application of brain machine interfaces in well-defined and circumscribed neurological disorders have demonstrated surprisingly positive effects. The application of BCIs to psychiatric and clinical–psychological problems, however, at present did not result in substantial improvement of complex behavioral disorders.
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References
Birbaumer N, Elbert T, Canavan A, Rockstroh B (1990) Slow potentials of the cerebral cortex and behavior. Physiol Rev 70:1–41
Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kübler A, Perelmouter J, Taub E, Flor H (1999) A spelling device for the paralysed. Nature 398:297–298
Birbaumer N, Ruiz S, Sitaram R (2013) Learned regulation of brain metabolism. Trends Cogn Sci 17(6):295–302
Buch ER, Shanechi AM, Fourkas AD, Weber C, Birbaumer N, Cohen LG (2012) Parietofrontal integrity determines neural modulation associated with grasping imagery after stroke. Brain 135:596–614
Chaudhary U, Bin X, Cohen L, Silvoni S, Birbaumer N (submitted) Unlocking the locked-in: brain communication in the completely locked-in state
Collinger JL, Wodlinger B, Downey JE, Wang W, Tyler-Kabara EC, Weber DJ, McMorland AJ, Velliste M, Boninger ML, Schwartz AB (2013) High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381(9866):557–564
Fetz EE (1969) Operant conditioning of cortical unit activity. Science 163:955–958
Gallegos-Ayala G, Furdea A, Takano K, Ruf CA, Flor H, Birbaumer N (2014) Brain communication in a completely locked-in patient using bedside near-infrared spectroscopy. Neurology 82:1–3
Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, Branner A, Chen D, Penn RD, Donoghue JP (2006) Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442:164–171
Hochberg LR, Bacher D, Jarosiewicz B, Masse NY, Simeral JD, Vogel J, Haddadin S, Liu J, Cash SS, van der Smagt P, Donoghue JP (2012) Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485:372–375
Koralek AC, Jin X, Long JD II, Costa RM, Carmena JM (2012). Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills. Nature 483(7389):331–5
Kotchoubey B, Strehl U, Uhlmann C, Holzapfel S, König M, Fröscher W et al (2001) Modification of slow cortical potentials in patients with refractory epilepsy: a controlled outcome study. Epilepsia 42(3):406–416
Kübler A, Birbaumer N (2008) Brain-computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients? Clin Neurophysiol 119:2658–2666
Ramos-Murguialday A et al (2013). Brain-machine-interface in chronic stroke rehabilitation: a controlled study. Ann Neurol 74,100–108
Wolf SL, Winstein CJ, Miller JP et al (2006) Effect of constraint-induced movement therapy on upper extremity function 3 to 9 months after stroke: the EXCITE randomized clinical trial. JAMA 296:2095–2104
Acknowledgements
Mit Förderung der Deutschen Forschungsgemeinschaft (DFG, Koselleck-Projekt), Neuroarbeitswissenschaft Baden-Württemberg und EMOIO-Projekt (BMBF Nr. 16SV7196), Stiftung Volkswagenwerk (VW), Eva und Horst Köhler-Stiftung, Baden-Württemberg-Stiftung.
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This document has been translated to English by Karin Moan.
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Birbaumer, N., Chaudhary, U. Learning from brain control: clinical application of brain–computer interfaces. e-Neuroforum 6, 87–95 (2015). https://doi.org/10.1007/s13295-015-0015-x
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DOI: https://doi.org/10.1007/s13295-015-0015-x