Historical perspectives, challenges, and future directions of implantable brain-computer interfaces for sensorimotor applications

Abstract

Almost 100 years ago experiments involving electrically stimulating and recording from the brain and the body launched new discoveries and debates on how electricity, movement, and thoughts are related. Decades later the development of brain-computer interface technology began, which now targets a wide range of applications. Potential uses include augmentative communication for locked-in patients and restoring sensorimotor function in those who are battling disease or have suffered traumatic injury. Technical and surgical challenges still surround the development of brain-computer technology, however, before it can be widely deployed. In this review we explore these challenges, historical perspectives, and the remarkable achievements of clinical study participants who have bravely forged new paths for future beneficiaries.

Fig. 1

The Utah Array™. (A) Flat 96 electrode array fabricated by etching a solid piece of silicon followed by metallization, insulation, and wire bonding processes to create a final assembly. (B) Slanted array created for recording at electrical activity at various penetration depths. Photographs provided by Blackrock Microsystems, Inc.

Fig. 2

BCI system for movement restoration in a paralyzed human study participant. (A) Cortical implant location, (B) muscle stimulation sleeve, (C) experimental setup, and (D) raster plot of neural activity (channel 37, Unit 1) for imagined/attempted wrist movements (extension, flexion, and radial/ulnar deviations) and the unit temporal response, (E) mean wavelet power for all trials shown (bottom) and mean power (+/− 1 std. dev.) is shown in pink (top). Reprinted with permission from: Bouton, Chad E., et al. “Restoring cortical control of functional movement in a human with quadriplegia.” Nature 533.7602 (2016): 247–250

Fig. 3

Functional movements achieved by a paralyzed study participant using an electronic neural bypass linking decoded brain activity to muscle activation in real-time. (A-F) Sequence of movements including opening of the hand, grasping a bottle with a cylindrical grasp, and stirring the contents with a pinch grasp. Reprinted with permission from: Bouton, Chad E., et al. “Restoring cortical control of functional movement in a human with quadriplegia.” Nature 533.7602 (2016): 247–250

Fig. 4

Object identification through stimulation-evoked tactile percepts. (A) Mapping of elicited tactile percepts and sensors from the virtual Modular Prosthetic Limb (vMPL) used for (B) grasping objects of varying shape. (C) ICMS amplitude was linearly modulated using different stimulation paradigms, each with a different weighting of sustained (β) and transient (훾) grip force. (D) Differences in the spatiotemporal tactile sensations restored through ICMS enabled the participant to identify the different objects. Image adapted and reproduced from (Osborn et al. 2021)

Fig. 5

A. and B. Self-reported sensory percepts in the hand upon sulcal stimulation in S1. All the sensory percepts reported by participant 1 and 2 respectively upon SEEG-mediated sulcal stimulation. The color of each electrode matches the color of the corresponding percept evoked. The third panels show a 3D brain slice showing the same SEEG electrodes. Black dashed line and white arrows denote the central sulcus. C. Heatmap shows distribution of percepts evoked by S1 sulcal stimulation pooled from two participants. Number of percepts covering a region of the hand were normalized to the maximal number of percepts covering any area of the hand (n = 5). Image reproduced from (Chandrasekaran et al. 2021)