Implanted cortical neuroprosthetics for speech and movement restoration

Abstract

Implanted cortical neuroprosthetics (ICNs) are medical devices developed to replace dysfunctional neural pathways by creating information exchange between the brain and a digital system which can facilitate interaction with the external world. Over the last decade, researchers have explored the application of ICNs for diverse conditions including blindness, aphasia, and paralysis. Both transcranial and endovascular approaches have been used to record neural activity in humans, and in a laboratory setting, high-performance decoding of the signals associated with speech intention has been demonstrated. Particular progress towards a device which can move into clinical practice has been made with ICNs focussed on the restoration of speech and movement. This article provides an overview of contemporary ICNs for speech and movement restoration, their mechanisms of action and the unique ethical challenges raised by the field.

Keywords: Brain–computer interface; Motor neuroprosthesis; Neurological disease; Neuromotor prosthesis; Neuroprosthetic; Neurotechnology.

Fig. 1

Categories of movement restoration and effector control that can be driven by a motor neuroprosthesis

Fig. 2

Illustration of devices in the four different anatomical compartments in which motor neuroprostheses have been implanted in human trials

Fig. 3

(a) A Utah Microelectrode Array (panel figure adapted from [14] used with permission) (b) WIMAGINE extradural electrode array (panel figure adapted from [27] used with permission); (c) Stentrode endovascular neuroprosthesis (panel figure from Synchron Corporation [28] used with permission); (d) Subdural electrode arrays (arrays in the panel figure are adapted from Ad-Tech Medical and used for seizure monitoring [29] used with permission) Figure adapted from sources referenced and images reproduced with permission

Fig. 4

Neural recordings from the first human user of a microelectrode array based neuroprosthetic (a) A well-isolated single unit recording from a single electrode (trace shows the superposition of 80 waveforms); (b) Over 80 seconds the participant was asked to imagine performing a series of movements in the arm contralateral to the array. Spiking activity of a recorded unit is shown along the top of the panel with the normalised integrated firing rate immediately below that. This unit demonstrates an increased firing frequency with the instruction to move hands apart/together; (c) Spike rates for two units recorded simultaneously during the performance of movement of the on screen neural cursor. The unit recorded in channel 1 demonstrates increased firing with the cue to move the cursor upwards but not downwards. Conversely, the unit recorded in channel 2 demonstrates increased firing following the cue to move the cursor downwards but not upwards; (d) Research technician and participant neural cursor traces during a 5 second period of the participant tracking the cursor; Panel figures adapted from [14] and used with permission. Channel numbers altered for simplicity of presentation Figure adapted from source referenced and images reproduced with permission

Fig. 5

(a) The Utrecht motor neuroprosthesis for communication demonstrated in a user with amyotrophic lateral sclerosis (panel adapted from and used with permission). (b) The proposed mechanism of the Lausanne Brain Spine Interface for movement restoration demonstrated in a user with spinal cord injury (panel adapted from [10] originally published under Creative Commons Attribution 4.0 International License). Both of these systems are fully implanted and could be used by the participant in their home environment Figure adapted from sources referenced and images reproduced with permission

Fig. 6

(a) The subdural based speech neuroprosthetic with avatar developed in the UCSF BRAVO study9 (panel figure adapted from [9] and used with permission) (b) The MEA based speech neuroprosthetic developed as part of the BrainGate consortium (panel figure adapted from [7] originally published under Creative Commons Attribution 4.0 International License). Both these decoders take advantage of language models to improve their accuracy Figure adapted from sources referenced and images reproduced with permission