Brain–Computer Interfaces Breaking New Ground in Patients with Neurologic Disability

VBCN - November 2016 Volume 3, No 3

Recent advances in brain–computer interfaces have moved these devices closer than ever to daily use, offering hope to millions of patients suffering from neurologic disability, said Karunesh Ganguly, MD, PhD, Assistant Professor of Neurology, University of California, San Francisco, at the 2016 American Academy of Neurology annual meeting.

“Proof-of-concept studies have impressively demonstrated direct brain control. Communication interfaces using invasive devices are approaching clinically desirable rates, and control of complex upper-limb prosthetic devices with the ability to improve function has already become a reality,” Dr Ganguly said.

Communication Interfaces

Although the early focus of communication interfaces has been on cursor control, more recent research has centered on optimized typing interfaces, said Dr Ganguly.1 Early-stage speech prosthetics are also in development, with the goal of decoding imagined speech to allow the voice to be heard again.2

In addition, researchers have worked to understand what constitutes an acceptable level of performance from the patient’s perspective. A recent study that surveyed 40 patients with spinal cord injury showed that a rate of >20 letters per minute at approximately 90% accuracy is desirable.3 “It’s a theoretical number, but it gives us a benchmark to shoot for,” said Dr Ganguly.

Using an innovative continuous calibration of decoders for stability, researchers achieved a typing rate of 15 to 22 characters per minute in 2 patients with amyotrophic lateral sclerosis (ALS) and in 2 patients with brainstem stroke.4

“The decoder is continuously deciphering the intention of the subject and trying to improve its parameters to keep up with any errors. These results are remarkable. We’re finally approaching the types of speeds that would be clinically relevant to our patients,” said Dr Ganguly.

Prosthetic Control

For patients with tetraplegia from a spinal cord injury or ALS, for example, the reaching and grasping functionalities are of primary importance. A neural interface system could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices, said Dr Ganguly.

Previous studies have demonstrated closed-loop control of a 4-degrees-of-freedom robotic arm in monkeys,5 and more recent research has shown that using neural interface systems to control physical devices is also possible in people with paralysis.6

Using a clinically validated scale known as the Action Research Arm Test, a patient with tetraplegia was able to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.6 Although robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, the results demonstrated the feasibility for people with tetraplegia, years after injury to the central nervous system, the researchers concluded.6

“Even for prosthetic limb controls, these performance rates would be very clinically meaningful. It gives you the sense of just how fast the field has moved in the last decade,” said Dr Ganguly.


  1. Bacher D, Jarosiewicz B, Masse NY, et al. Neural point-and-click communication by a person with incomplete locked-in syndrome. Neurorehabil Neural Repair. 2015;29:462-471.
  2. Brumberg JS, Wright EJ, Andreasen DS, et al. Classification of intended phoneme production from chronic intracortical microelectrode recordings in speech-motor cortex. Front Neurosci. 2011;5:65.
  3. Huggins JE, Moinuddin AA, Chiodo AE, Wren PA. What would brain-computer interface users want: opinions and priorities of potential users with spinal cord injury. Arch Phys Med Rehabil. 2015;96(3 suppl):S38-S45.e5.
  4. Jarosiewicz B, Sarma AA, Bacher D, et al. Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface. Sci Transl Med. 2015;7:313ra179.
  5. Velliste M, Perel S, Spalding MC, et al. Cortical control of a prosthetic arm for self-feeding. Nature. 2008;453:1098-1101.
  6. Collinger JL, Wodlinger B, Downey JE, et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet. 2013;381:557-564.

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