Optimal space-time precoding of artificial sensory feedback through mutichannel microstimulation in bi-directional brain-machine interfaces

Abstract

Brain-machine interfaces (BMIs) aim to restore lost sensorimotor and cognitive function in subjects with severe neurological deficits. In particular, lost somatosensory function may be restored by artificially evoking patterns of neural activity through microstimulation to induce perception of tactile and proprioceptive feedback to the brain about the state of the limb. Despite an early proof of concept that subjects could learn to discriminate a limited vocabulary of intracortical microstimulation (ICMS) patterns that instruct the subject about the state of the limb, the dynamics of a moving limb are unlikely to be perceived by an arbitrarily-selected, discrete set of static microstimulation patterns, raising questions about the generalization and the scalability of this approach. In this work, we propose a microstimulation protocol intended to activate optimally the ascending somatosensory pathway. The optimization is achieved through a space-time precoder that maximizes the mutual information between the sensory feedback indicating the limb state and the cortical neural response evoked by thalamic microstimulation. Using a simplified multi-input multi-output model of the thalamocortical pathway, we show that this optimal precoder can deliver information more efficiently in the presence of noise compared to suboptimal precoders that do not account for the afferent pathway structure and/or cortical states. These results are expected to enhance the way microstimulation is used to induce somatosensory perception during sensorimotor control of artificial devices or paralyzed limbs.

Figure 1

Topology of the thalamo-cortical network modeling the information transmission in an afferent pathway. PY: cortical pyramidal cells; IN: cortical inhibitory interneurons; RE: inhibitory reticular cells; TC: excitatory thalamocortical cells.

Figure 2

Space-time precoding of artificial sensory feedback. The space-time precoder provides a continuous time translation between state of the prosthetic device and the stimulator. The variables are as follows: s is the artificial sensory feedback, u is the frequency of stimulation, y is the neuronal response, and z is the noise.

Figure 3

Numerical examples of the design of space-time precoders. The neural circuit under stimulation has four simulation inputs and eight measured outputs. (a) The log of the frequency representation of the input signals after decomposition. (b) Optimal power allocation over frequency and independent channels after decomposition. (c) The space-time precoding filter coefficients from sensory signals s₁(n) and s₂(n) to stimulation inputs u₁(n) and u₂(n). (d) The Effects of precoding on the spread of the evoked spatiotemporal patterns projected along the first and the second principal components. (e) The statistics of the Euclidean distance between the nearest neighbors. The distance between nearest neighbors are 204 ± 56 for the optimal precoder, 192 ± 37 for the suboptimal precoder, and 176 ± 34 for the identity precoder. (f) The distribution of power along the eigenvalues of the system output.

Figure 4

The effects of noise power on achievable information rates. The power constraint P₀ is kept the same for all power scaling factors. Here we define noise power scaling factor, α, as in y = Gu + αz.

Figure 5

The effects of space-time precoding on decoding performance. The system has four simulation inputs and eight measured outputs. (a) The decoded sensory signals under optimal (left panels) and suboptimal (right panels) precoding. Total four sensory signals acc1, acc2, vel1, and vel2 are precoded. (b) The mean squared decoding error for each sensory signal and the overall mean squared decoding error for all sensory signals.

Figure 6

The differences in performance between the optimal and suboptimal precoders for various selections of input neurons. A unique set of input neurons was randomly selected for trial shown here.