Brain–computer interfaces (BCIs) are starting to prove their efficacy as assistive and rehabilitative technologies in patients with severe motor impairments
BCIs can be invasive or noninvasive, and designed to detect and decode a variety of brain signals
Assistive BCIs are intended to enable paralyzed patients to communicate or control external robotic devices; rehabilitative BCIs are intended to facilitate neural recovery
EEG-based BCIs have enabled some paralyzed patients to communicate, but near-infrared spectroscopy combined with a classical conditioning paradigm is the only successful approach for complete locked-in syndrome
The combination of EEG-based BCIs with behavioural physiotherapy is a feasible option for rehabilitation in stroke; the approach is to induce neuroplasticity and restore lost function after stroke
There is an urgent need for more large randomized controlled clinical trials using invasive and noninvasive BCIs with long-term follow-ups in patients rather than healthy populations
Brain–computer interfaces (BCIs) use brain activity to control external devices, thereby enabling severely disabled patients to interact with the environment. A variety of invasive and noninvasive techniques for controlling BCIs have been explored, most notably EEG, and more recently, near-infrared spectroscopy. Assistive BCIs are designed to enable paralyzed patients to communicate or control external robotic devices, such as prosthetics; rehabilitative BCIs are designed to facilitate recovery of neural function. In this Review, we provide an overview of the development of BCIs and the current technology available before discussing experimental and clinical studies of BCIs. We first consider the use of BCIs for communication in patients who are paralyzed, particularly those with locked-in syndrome or complete locked-in syndrome as a result of amyotrophic lateral sclerosis. We then discuss the use of BCIs for motor rehabilitation after severe stroke and spinal cord injury. We also describe the possible neurophysiological and learning mechanisms that underlie the clinical efficacy of BCIs.
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The authors are funded by Deutsche Forschungsgemeinschaft (DFG, Bi195, Kosellek), Stiftung Volkswagenwerk (VW), German Ministry of Education and Research (BMBF, grant number MOTOR-BIC (FKZ 136W0053), Ministry of Science, Research and the Arts of Baden Wüttemberg (Az: 32–729.63-0/5-5), Baden-Württemberg Stiftung (ROB-1), EMOIO from the Federal Ministry of Education and Research (BMBF, 524-4013-16SV7196) and Eva and Horst Köhler-Stiftung, (Berlin), EU (Horizon 2020) grant “Brain Train” and “Luminous”, Brain Products, Munich, Germany, and the Wyss Foundation, Geneva, Switzerland.
The authors declare no competing financial interests.
- Alpha waves
Neural oscillations in the frequency range of 8–13 Hz, indicating widespread inhibitory activity in neuronal tissue.
- Instrumental learning
A type of learning in which the strength of a behaviour or a physiological response is modified by its consequences (reward or punishment).
- Local field potentials
Graded neuroelectrical changes in voltage, generated by the summed synaptic currents flowing from multiple nearby neurons within a small volume of nervous tissue, recorded from inserted microelectrodes.
- Single-unit acitivity
Action potentials of single neurons, recorded using inserted microelectrodes
- Multi-unit activity
Action potentials of multiple neurons,recorded using an array of multiple microelectrodes.
- Cortical preparation
Cortical preparation occurs before a cognitive, motor or emotional response, and is detectable with EEG as a negatively polarized voltage shift.
Contingency is an associative connection between stimuli or responses that are usually paired within a short time period of milliseconds to seconds.
- Classical conditioning
Classical conditioning, also called Pavlovian conditioning, is a learning process in which two stimuli are repeatedly paired until one elicits a reflexive behavioural or physiological response that relates to the other.
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Chaudhary, U., Birbaumer, N. & Ramos-Murguialday, A. Brain–computer interfaces for communication and rehabilitation. Nat Rev Neurol 12, 513–525 (2016). https://doi.org/10.1038/nrneurol.2016.113
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