Brain–computer interfaces for communication and rehabilitation

By U. Chaudhary, Niels Birbaumer, Ander Ramos-Murguialday. Nature Reviews Neurology, 2016

836 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/c1b86030f6f8447d38029062d388c0ab23410d5b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Brenna D. Argall
2. Dorsa Sadigh

In Cellular and Molecular Neuroscience

1. Jakob S. Møller
2. Helle Klingenberg Iversen
3. Sadasivan K. Puthusserypady
4. Brenna D. Argall
5. Hauke Dose

In Cognitive Neuroscience

1. Jakob S. Møller
2. Helle Klingenberg Iversen
3. Sadasivan K. Puthusserypady
4. Brenna D. Argall
5. Hauke Dose

Citations in the corpus

• An end-to-end deep learning approach to MI-EEG signal classification for BCIs (2018, background): “, 2009)), to stem cells and pharmacological therapy (Chaudhary et al., 2016), to the usage of Brain-Computer Interface (BCI) systems have emerged to tackle the limitations of current stroke rehabilitation strategies/methods.”
• Autonomy in Rehabilitation Robotics: An Intersection (2018, positive): “In addition to these noninvasive neural interfaces, cortical neural interfaces (47, 48) also might be used to operate wheelchairs and wheelchair-mounted robotic arms—with transformative potential for patients with locked-in syndrome, for example.”
• Progress in Neuroengineering for brain repair: New challenges and open issues (2018, background): “A non-invasive recording technique like electroencephalography (EEG) has a low spatial resolution and signalto-noise ratio but no clinical risk (Chaudhary et al., 2016).”