Challenges and Opportunities for Next-Generation Intracortically Based Neural Prostheses
- 20 January 2011
- journal article
- review article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Biomedical Engineering
- Vol. 58 (7), 1891-1899
- https://doi.org/10.1109/tbme.2011.2107553
Abstract
Neural prosthetic systems aim to help disabled patients by translating neural signals from the brain into control signals for guiding computer cursors, prosthetic arms, and other assistive devices. Intracortical electrode arrays measure action potentials and local field potentials from individual neurons, or small populations of neurons, in the motor cortices and can provide considerable information for controlling prostheses. Despite several compelling proof-of-concept laboratory animal experiments and an initial human clinical trial, at least three key challenges remain which, if left unaddressed, may hamper the translation of these systems into widespread clinical use. We review these challenges: achieving able-bodied levels of performance across tasks and across environments, achieving robustness across multiple decades, and restoring able-bodied quality proprioception and somatosensation. We also describe some emerging opportunities for meeting these challenges. If these challenges can be largely or fully met, intracortically based neural prostheses may achieve true clinical viability and help increasing numbers of disabled patients.Keywords
This publication has 81 references indexed in Scilit:
- An optogenetic toolbox designed for primatesNature Neuroscience, 2011
- Cortical Preparatory Activity: Representation of Movement or First Cog in a Dynamical Machine?Neuron, 2010
- Evolution of brain–computer interface: action potentials, local field potentials and electrocorticogramsCurrent Opinion in Neurobiology, 2010
- Autonomous head-mounted electrophysiology systems for freely behaving primatesCurrent Opinion in Neurobiology, 2010
- Methods for estimating neural firing rates, and their application to brain–machine interfacesNeural Networks, 2009
- Direct Activation of Sparse, Distributed Populations of Cortical Neurons by Electrical MicrostimulationNeuron, 2009
- Bias, optimal linear estimation, and the differences between open-loop simulation and closed-loop performance of spiking-based brain–computer interface algorithmsNeural Networks, 2009
- Millisecond-Timescale Optical Control of Neural Dynamics in the Nonhuman Primate BrainNeuron, 2009
- Functional network reorganization during learning in a brain-computer interface paradigmProceedings of the National Academy of Sciences, 2008
- Direct control of paralysed muscles by cortical neuronsNature, 2008