High-performance neuroprosthetic control by an individual with tetraplegia.

By J. Collinger, B. Wodlinger, J. Downey, Wei Wang, E. Tyler-Kabara, D. Weber, Angus J. C. McMorland, M. Velliste, M. Boninger, Andrew B. Schwartz. The Lancet, 2013

1,707 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/4e3680638f2405ced5fd86d7b69ec999aede1e0d

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In Biomedical Engineering

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

EEG Source Imaging Enhances the Decoding of Complex Right Hand Motor Imagery Tasks (2015, background): “BCIs using intracortically implanted electrode arrays have allowed subjects with severe damage to their descending motor circuitry to successfully control robotic prosthetics with anthropomorphic movement to complete common everyday tasks [2],”
Long-term stability of cortical population dynamics underlying consistent behavior (2020, positive): “This is of particular interest within the field of brain-computer interfaces (BCIs) that seek to “decode” cortical activity to obtain control signals for computer cursors, robots, or prostheses that reanimate paralyzed limbs 23–25 .”
Cortical control of arm movements: a dynamical systems perspective. (2013, background): “Many fundamental principles have been discovered, and this basic scientific knowledge has led to rapid advances, including early clinical trials of cortically controlled neural prostheses for paralyzed participants (Collinger et al. 2013; Hochberg et al. 2006, 2012).”
High-performance brain-to-text communication via handwriting (2021, background): “…performance so far compared to other recording technologies (for example, EEG or electrocorticography) for restoring communication 7 , arm control 2,5 ) was similar for characters and straight lines, but the temporal dimensionality was more than twice as high for characters, suggesting that more…”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, methodology): “Even the former proponents of small ensembles have now switched their approaches to embrace large neuronal samples as the only viable approach for clinically-relevant BMI applications (152, 360, 362).”
An Integrated Brain-Machine Interface Platform With Thousands of Channels (2019, background): “For example, researchers have demonstrated human neuroprosthetic control of computer cursors [1, 2, 3], robotic limbs [4, 5], and speech synthesizers [6] using no more than 256 electrodes.”
Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives (2014, positive): “also been implanted in several severely disabled individuals for multidimensional control of a computer cursor [9], [10] or a robotic arm [11], [12].”
Control strategies for active lower extremity prosthetics and orthotics: a review (2015, positive): “Intracortical electrode arrays have been successfully demonstrated to allow control of multi-degree-of-freedom reach and grasp movements with robotic arms in tetraplegic subjects [55,56],”
Machine Learning for Neural Decoding (2017, background): “, 2002), robotic arms (Collinger et al., 2013), and muscles (Ethier et al.”
Design of a highly biomimetic anthropomorphic robotic hand towards artificial limb regeneration (2016, methodology): “…(EMG) signals collected from the residual limb or targeted muscle reinnervation regions [4]; or using intrusive methods like directly implanting microelectrodes at the motor cortex of the brain [5] or cuffing peripheral nerves with miniaturized electrodes to collect control inputs [6].”
Intracortical microstimulation of human somatosensory cortex (2016, background): “For example, we demonstrated that a person with tetraplegia could simultaneously control up to 10 degrees of freedom of an anthropomorphic robotic limb using a cortical neural interface (8, 9).”
Brain-machine interfaces in neurorehabilitation of stroke. (2015, positive): “…et al., 2012a), decoding of high gamma band and action potentials (single-or multi-unit) (Mehring et al., 2004; Stark and Abeles, 2007; Flint et al., 2012b) allowed for control of high degree of freedom prosthetic limbs (Hochberg et al., 2012; Collinger et al., 2013) or FES (Ethier et al., 2012).”
Speech synthesis from neural decoding of spoken sentences (2019, background): “Neural prosthetic control was first demonstrated in participants without disabilities 33,34,35 before translating the technology to participants with tetraplegia 36,37,38,39 .”