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Pandarinath C, Bensmaia SJ. The science and engineering behind sensitized brain-controlled bionic hands. Physiol Rev 2022; 102:551-604. [PMID: 34541898 PMCID: PMC8742729 DOI: 10.1152/physrev.00034.2020] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/07/2021] [Accepted: 09/13/2021] [Indexed: 12/13/2022] Open
Abstract
Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.
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Affiliation(s)
- Chethan Pandarinath
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia
- Department of Neurosurgery, Emory University, Atlanta, Georgia
| | - Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois
- Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, University of Chicago, Chicago, Illinois
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Jiang X, Ryu SI, Shenoy KV, Kao JC. Single Neuron Firing Rate Statistics in Motor Cortex During Execution and Observation of Movement. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:981-986. [PMID: 30440555 DOI: 10.1109/embc.2018.8512445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Mirror neurons, which fire during both the execution and observation of movement, are believed to play an important role in motor processing and learning. However, much work still remains to understand the similarities and differences in how these neurons compute in the motor cortex during movement execution and observation. Here, we performed experiments where a monkey both executes and observes a center-out-and-back task within the same experimental session. By recording from putatively the same neural population, we were able to analyze and compare single neuron statistics between movement execution and observation. We found that a majority of neurons in the primary motor cortex (M1) and dorsal premotor cortex (PMd) have statistically different firing rate statistics between movement execution and observation. As a result of this difference, we then wondered if neurons during movement observation exhibited a similar characteristic to those during movement execution: changing of preferred directions as a function of movement speed. Interestingly, we found that while observed movement speed is encoded in the neural population, it only alters a small proportion of the neuron's firing rate statistics. These results suggest that neural populations in Ml and PMd process information related to movement differently between execution and observation.
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Even-Chen N, Stavisky SD, Kao JC, Ryu SI, Shenoy KV. Augmenting intracortical brain-machine interface with neurally driven error detectors. J Neural Eng 2017; 14:066007. [PMID: 29130452 PMCID: PMC5742283 DOI: 10.1088/1741-2552/aa8dc1] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
OBJECTIVE Making mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain-machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby consuming time and thus hindering performance. We hypothesized that neural correlates of the user perceiving the mistake could be used by the BMI to automatically correct errors. However, it was unknown whether intracortical outcome error signals were present in the premotor and primary motor cortices, brain regions successfully used for intracortical BMIs. APPROACH We report here for the first time a putative outcome error signal in spiking activity within these cortices when rhesus macaques performed an intracortical BMI computer cursor task. MAIN RESULTS We decoded BMI trial outcomes shortly after and even before a trial ended with 96% and 84% accuracy, respectively. This led us to develop and implement in real-time a first-of-its-kind intracortical BMI error 'detect-and-act' system that attempts to automatically 'undo' or 'prevent' mistakes. The detect-and-act system works independently and in parallel to a kinematic BMI decoder. In a challenging task that resulted in substantial errors, this approach improved the performance of a BMI employing two variants of the ubiquitous Kalman velocity filter, including a state-of-the-art decoder (ReFIT-KF). SIGNIFICANCE Detecting errors in real-time from the same brain regions that are commonly used to control BMIs should improve the clinical viability of BMIs aimed at restoring motor function to people with paralysis.
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Affiliation(s)
- Nir Even-Chen
- Department of Electrical Engineering at Stanford University, Stanford, CA 94305 USA
| | - Sergey D. Stavisky
- Department of Electrical Engineering at Stanford University, Stanford, CA 94305 USA
- Department of Neurosurgery, Stanford University, Stanford, CA 94305 USA
| | - Jonathan C. Kao
- Department of Electrical Engineering at Stanford University, Stanford, CA 94305 USA
- Department of Electrical Engineering, University of California Los Angeles, Los Angeles, CA 90095 USA
| | - Stephen I. Ryu
- Department of Electrical Engineering at Stanford University, Stanford, CA 94305 USA
- Department of Neurosurgery at Palo Alto Medical Foundation, Palo Alto, CA, USA
| | - Krishna V. Shenoy
- Department of Electrical Engineering at Stanford University, Stanford, CA 94305 USA
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA 94305
- The Bio-X Program, Stanford University, Stanford, CA 94305
- The Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305
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Kao JC, Ryu SI, Shenoy KV. Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces. Sci Rep 2017; 7:7395. [PMID: 28784984 PMCID: PMC5547077 DOI: 10.1038/s41598-017-06029-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 06/07/2017] [Indexed: 01/09/2023] Open
Abstract
Intracortical brain-machine interfaces (BMIs) aim to restore lost motor function to people with neurological deficits by decoding neural activity into control signals for guiding prostheses. An important challenge facing BMIs is that, over time, the number of neural signals recorded from implanted multielectrode arrays will decline and result in a concomitant decrease of BMI performance. We sought to extend BMI lifetime by developing an algorithmic technique, implemented entirely in software, to improve performance over state-of-the-art algorithms as the number of recorded neural signals decline. Our approach augments the decoder by incorporating neural population dynamics remembered from an earlier point in the array lifetime. We demonstrate, in closed-loop experiments with two rhesus macaques, that after the loss of approximately 60% of recording electrodes, our approach outperforms state-of-the-art decoders by a factor of 3.2× and 1.7× (corresponding to a 46% and 22% recovery of maximal performance). Further, our results suggest that neural population dynamics in motor cortex are invariant to the number of recorded neurons. By extending functional BMI lifetime, this approach increases the clinical viability of BMIs.
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Affiliation(s)
- Jonathan C Kao
- Department of Electrical Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA.,Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA.,Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, 94301, USA
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA. .,Department of Neurobiology, Stanford University, Stanford, CA, 94305, USA. .,Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA. .,Neurosciences Program, Stanford University, Stanford, CA, 94305, USA. .,Howard Hughes Medical Institute, Stanford University, Stanford, CA, 94305, USA.
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Nuyujukian P, Kao JC, Ryu SI, Shenoy KV. A Non-Human Primate Brain-Computer Typing Interface. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2017; 105:66-72. [PMID: 33746239 PMCID: PMC7970827 DOI: 10.1109/jproc.2016.2586967] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Brain-computer interfaces (BCIs) record brain activity and translate the information into useful control signals. They can be used to restore function to people with paralysis by controlling end effectors such as computer cursors and robotic limbs. Communication neural prostheses are BCIs that control user interfaces on computers or mobile devices. Here we demonstrate a communication prosthesis by simulating a typing task with two rhesus macaques implanted with electrode arrays. The monkeys used two of the highest known performing BCI decoders to type out words and sentences when prompted one symbol/letter at a time. On average, Monkeys J and L achieved typing rates of 10.0 and 7.2 words per minute (wpm), respectively, copying text from a newspaper article using a velocity-only two dimensional BCI decoder with dwell-based symbol selection. With a BCI decoder that also featured a discrete click for key selection, typing rates increased to 12.0 and 7.8 wpm. These represent the highest known achieved communication rates using a BCI. We then quantified the relationship between bitrate and typing rate and found it approximately linear: typing rate in wpm is nearly three times bitrate in bits per second. We also compared the metrics of achieved bitrate and information transfer rate and discuss their applicability to real-world typing scenarios. Although this study cannot model the impact of cognitive load of word and sentence planning, the findings here demonstrate the feasibility of BCIs to serve as communication interfaces and represent an upper bound on the expected achieved typing rate for a given BCI throughput.
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Affiliation(s)
- Paul Nuyujukian
- Neurosurgery Department, the Electrical Engineering Department, the Bioengineering Department, and Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305 USA
| | - Jonathan C Kao
- Electrical Engineering Department, Stanford University, Stanford, CA 94305 USA
| | - Stephen I Ryu
- Electrical Engineering Department, Stanford University, Stanford, CA 94305 USA and also with Palo Alto Medical Foundation, Palo Alto, CA 94550 USA
| | - Krishna V Shenoy
- Electrical Engineering Department, the Bioengineering Department, the Neurobiology Department, and Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305 USA and also with Howard Hughes Medical Institute, Chevy Chase, MD 20815 USA
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7
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Making brain-machine interfaces robust to future neural variability. Nat Commun 2016; 7:13749. [PMID: 27958268 PMCID: PMC5159828 DOI: 10.1038/ncomms13749] [Citation(s) in RCA: 92] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2016] [Accepted: 10/29/2016] [Indexed: 01/15/2023] Open
Abstract
A major hurdle to clinical translation of brain–machine interfaces (BMIs) is that current decoders, which are trained from a small quantity of recent data, become ineffective when neural recording conditions subsequently change. We tested whether a decoder could be made more robust to future neural variability by training it to handle a variety of recording conditions sampled from months of previously collected data as well as synthetic training data perturbations. We developed a new multiplicative recurrent neural network BMI decoder that successfully learned a large variety of neural-to-kinematic mappings and became more robust with larger training data sets. Here we demonstrate that when tested with a non-human primate preclinical BMI model, this decoder is robust under conditions that disabled a state-of-the-art Kalman filter-based decoder. These results validate a new BMI strategy in which accumulated data history are effectively harnessed, and may facilitate reliable BMI use by reducing decoder retraining downtime. Brain-machine interfaces (BMI) depend on algorithms to decode neural signals, but these decoders cope poorly with signal variability. Here, authors report a BMI decoder which circumvents these problems by using a large and perturbed training dataset to improve performance with variable neural signals.
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Even-Chen N, Stavisky SD, Kao JC, Ryu SI, Shenoy KV. Auto-deleting brain machine interface: Error detection using spiking neural activity in the motor cortex. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:71-5. [PMID: 26736203 DOI: 10.1109/embc.2015.7318303] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Brain machine interfaces (BMIs) aim to assist people with paralysis by increasing their independence and ability to communicate, e.g., by using a cursor-based virtual keyboard. Current BMI clinical trials are hampered by modest performance that causes selection of wrong characters (errors) and thus reduces achieved typing rate. If it were possible to detect these errors without explicit knowledge of the task goal, this could be used to automatically "undo" wrong selections or even prevent upcoming wrong selections. We decoded imminent or recent errors during closed-loop BMI control from intracortical spiking neural activity. In our experiment, a non-human primate controlled a neurally-driven BMI cursor to acquire targets on a grid, which simulates a virtual keyboard. In offline analyses of this closed-loop BMI control data, we identified motor cortical neural signals indicative of task error occurrence. We were able to detect task outcomes (97% accuracy) and even predict upcoming task outcomes (86% accuracy) using neural activity alone. This novel strategy may help increase the performance and clinical viability of BMIs.
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Kao JC, Nuyujukian P, Ryu SI, Shenoy KV. A High-Performance Neural Prosthesis Incorporating Discrete State Selection With Hidden Markov Models. IEEE Trans Biomed Eng 2016; 64:935-945. [PMID: 27337709 DOI: 10.1109/tbme.2016.2582691] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Communication neural prostheses aim to restore efficient communication to people with motor neurological injury or disease by decoding neural activity into control signals. These control signals are both analog (e.g., the velocity of a computer mouse) and discrete (e.g., clicking an icon with a computer mouse) in nature. Effective, high-performing, and intuitive-to-use communication prostheses should be capable of decoding both analog and discrete state variables seamlessly. However, to date, the highest-performing autonomous communication prostheses rely on precise analog decoding and typically do not incorporate high-performance discrete decoding. In this report, we incorporated a hidden Markov model (HMM) into an intracortical communication prosthesis to enable accurate and fast discrete state decoding in parallel with analog decoding. In closed-loop experiments with nonhuman primates implanted with multielectrode arrays, we demonstrate that incorporating an HMM into a neural prosthesis can increase state-of-the-art achieved bitrate by 13.9% and 4.2% in two monkeys ( ). We found that the transition model of the HMM is critical to achieving this performance increase. Further, we found that using an HMM resulted in the highest achieved peak performance we have ever observed for these monkeys, achieving peak bitrates of 6.5, 5.7, and 4.7 bps in Monkeys J, R, and L, respectively. Finally, we found that this neural prosthesis was robustly controllable for the duration of entire experimental sessions. These results demonstrate that high-performance discrete decoding can be beneficially combined with analog decoding to achieve new state-of-the-art levels of performance.
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Affiliation(s)
| | - Paul Nuyujukian
- Department of Electrical Engineering, Department of Bioengineering, and School of Medicine and NeurosurgeryStanford University
| | - Stephen I Ryu
- Department of Electrical EngineeringStanford University
| | - Krishna V Shenoy
- Department of Electrical Engineering, Department of Bioengineering, and Department of NeurobiologyStanford University
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Golub MD, Chase SM, Batista AP, Yu BM. Brain-computer interfaces for dissecting cognitive processes underlying sensorimotor control. Curr Opin Neurobiol 2016; 37:53-58. [PMID: 26796293 DOI: 10.1016/j.conb.2015.12.005] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Revised: 12/16/2015] [Accepted: 12/17/2015] [Indexed: 01/19/2023]
Abstract
Sensorimotor control engages cognitive processes such as prediction, learning, and multisensory integration. Understanding the neural mechanisms underlying these cognitive processes with arm reaching is challenging because we currently record only a fraction of the relevant neurons, the arm has nonlinear dynamics, and multiple modalities of sensory feedback contribute to control. A brain-computer interface (BCI) is a well-defined sensorimotor loop with key simplifying advantages that address each of these challenges, while engaging similar cognitive processes. As a result, BCI is becoming recognized as a powerful tool for basic scientific studies of sensorimotor control. Here, we describe the benefits of BCI for basic scientific inquiries and review recent BCI studies that have uncovered new insights into the neural mechanisms underlying sensorimotor control.
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Affiliation(s)
- Matthew D Golub
- Department of Electrical and Computer Engineering, Carnegie Mellon University, United States; Center for the Neural Basis of Cognition, Carnegie Mellon University & University of Pittsburgh, United States
| | - Steven M Chase
- Department of Biomedical Engineering, Carnegie Mellon University, United States; Center for the Neural Basis of Cognition, Carnegie Mellon University & University of Pittsburgh, United States
| | - Aaron P Batista
- Center for the Neural Basis of Cognition, Carnegie Mellon University & University of Pittsburgh, United States; Department of Bioengineering, University of Pittsburgh, United States; Systems Neuroscience Institute, University of Pittsburgh, United States
| | - Byron M Yu
- Department of Electrical and Computer Engineering, Carnegie Mellon University, United States; Department of Biomedical Engineering, Carnegie Mellon University, United States; Center for the Neural Basis of Cognition, Carnegie Mellon University & University of Pittsburgh, United States
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11
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Abstract
Increasing evidence suggests that neural population responses have their own internal drive, or dynamics, that describe how the neural population evolves through time. An important prediction of neural dynamical models is that previously observed neural activity is informative of noisy yet-to-be-observed activity on single-trials, and may thus have a denoising effect. To investigate this prediction, we built and characterized dynamical models of single-trial motor cortical activity. We find these models capture salient dynamical features of the neural population and are informative of future neural activity on single trials. To assess how neural dynamics may beneficially denoise single-trial neural activity, we incorporate neural dynamics into a brain–machine interface (BMI). In online experiments, we find that a neural dynamical BMI achieves substantially higher performance than its non-dynamical counterpart. These results provide evidence that neural dynamics beneficially inform the temporal evolution of neural activity on single trials and may directly impact the performance of BMIs. In online experiments with monkeys the authors demonstrate, for the first time, that incorporating neural dynamics substantially improves brain–machine interface performance. This result is consistent with a framework hypothesizing that motor cortex is a dynamical machine that generates movement.
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12
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Neuroplasticity subserving the operation of brain-machine interfaces. Neurobiol Dis 2015; 83:161-71. [PMID: 25968934 DOI: 10.1016/j.nbd.2015.05.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2014] [Revised: 04/27/2015] [Accepted: 05/01/2015] [Indexed: 01/16/2023] Open
Abstract
Neuroplasticity is key to the operation of brain machine interfaces (BMIs)-a direct communication pathway between the brain and a man-made computing device. Whereas exogenous BMIs that associate volitional control of brain activity with neurofeedback have been shown to induce long lasting plasticity, endogenous BMIs that use prolonged activity-dependent stimulation--and thus may curtail the time scale that governs natural sensorimotor integration loops--have been shown to induce short lasting plasticity. Here we summarize recent findings from studies using both categories of BMIs, and discuss the fundamental principles that may underlie their operation and the longevity of the plasticity they induce. We draw comparison to plasticity mechanisms known to mediate natural sensorimotor skill learning and discuss principles of homeostatic regulation that may constrain endogenous BMI effects in the adult mammalian brain. We propose that BMIs could be designed to facilitate structural and functional plasticity for the purpose of re-organization of target brain regions and directed augmentation of sensorimotor maps, and suggest possible avenues for future work to maximize their efficacy and viability in clinical applications.
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Bakken TE, Miller JA, Luo R, Bernard A, Bennett JL, Lee CK, Bertagnolli D, Parikshak NN, Smith KA, Sunkin SM, Amaral DG, Geschwind DH, Lein ES. Spatiotemporal dynamics of the postnatal developing primate brain transcriptome. Hum Mol Genet 2015; 24:4327-39. [PMID: 25954031 PMCID: PMC4492396 DOI: 10.1093/hmg/ddv166] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Accepted: 05/05/2015] [Indexed: 01/06/2023] Open
Abstract
Developmental changes in the temporal and spatial regulation of gene expression drive the emergence of normal mature brain function, while disruptions in these processes underlie many neurodevelopmental abnormalities. To solidify our foundational knowledge of such changes in a primate brain with an extended period of postnatal maturation like in human, we investigated the whole-genome transcriptional profiles of rhesus monkey brains from birth to adulthood. We found that gene expression dynamics are largest from birth through infancy, after which gene expression profiles transition to a relatively stable state by young adulthood. Biological pathway enrichment analysis revealed that genes more highly expressed at birth are associated with cell adhesion and neuron differentiation, while genes more highly expressed in juveniles and adults are associated with cell death. Neocortex showed significantly greater differential expression over time than subcortical structures, and this trend likely reflects the protracted postnatal development of the cortex. Using network analysis, we identified 27 co-expression modules containing genes with highly correlated expression patterns that are associated with specific brain regions, ages or both. In particular, one module with high expression in neonatal cortex and striatum that decreases during infancy and juvenile development was significantly enriched for autism spectrum disorder (ASD)-related genes. This network was enriched for genes associated with axon guidance and interneuron differentiation, consistent with a disruption in the formation of functional cortical circuitry in ASD.
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Affiliation(s)
| | | | - Rui Luo
- Human Genetics Program, Department of Neurology and Semel Institute, David Geffen School of Medicine, UC, Los Angeles, Los Angeles, CA, USA and
| | - Amy Bernard
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jeffrey L Bennett
- Department of Psychiatry and Behavioral Science and M.I.N.D. Institute, UC Davis, Sacramento, CA, USA
| | | | | | - Neelroop N Parikshak
- Human Genetics Program, Department of Neurology and Semel Institute, David Geffen School of Medicine, UC, Los Angeles, Los Angeles, CA, USA and
| | | | | | - David G Amaral
- Department of Psychiatry and Behavioral Science and M.I.N.D. Institute, UC Davis, Sacramento, CA, USA
| | - Daniel H Geschwind
- Human Genetics Program, Department of Neurology and Semel Institute, David Geffen School of Medicine, UC, Los Angeles, Los Angeles, CA, USA and
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, USA,
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Stavisky SD, Kao JC, Nuyujukian P, Ryu SI, Shenoy KV. A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes. J Neural Eng 2015; 12:036009. [PMID: 25946198 DOI: 10.1088/1741-2560/12/3/036009] [Citation(s) in RCA: 86] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
OBJECTIVE Brain-machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI. APPROACH Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together. MAIN RESULTS LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor. SIGNIFICANCE These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs.
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15
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Sadtler PT, Ryu SI, Tyler-Kabara EC, Yu BM, Batista AP. Brain-computer interface control along instructed paths. J Neural Eng 2015; 12:016015. [PMID: 25605498 DOI: 10.1088/1741-2560/12/1/016015] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
OBJECTIVE Brain-computer interfaces (BCIs) are being developed to assist paralyzed people and amputees by translating neural activity into movements of a computer cursor or prosthetic limb. Here we introduce a novel BCI task paradigm, intended to help accelerate improvements to BCI systems. Through this task, we can push the performance limits of BCI systems, we can quantify more accurately how well a BCI system captures the user's intent, and we can increase the richness of the BCI movement repertoire. APPROACH We have implemented an instructed path task, wherein the user must drive a cursor along a visible path. The instructed path task provides a versatile framework to increase the difficulty of the task and thereby push the limits of performance. Relative to traditional point-to-point tasks, the instructed path task allows more thorough analysis of decoding performance and greater richness of movement kinematics. MAIN RESULTS We demonstrate that monkeys are able to perform the instructed path task in a closed-loop BCI setting. We further investigate how the performance under BCI control compares to native arm control, whether users can decrease their movement variability in the face of a more demanding task, and how the kinematic richness is enhanced in this task. SIGNIFICANCE The use of the instructed path task has the potential to accelerate the development of BCI systems and their clinical translation.
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Affiliation(s)
- P T Sadtler
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA. Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, USA. Systems Neuroscience Institute, University of Pittsburgh, Pittsburgh, PA, USA
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16
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Nuyujukian P, Kao JC, Fan JM, Stavisky SD, Ryu SI, Shenoy KV. Performance sustaining intracortical neural prostheses. J Neural Eng 2014; 11:066003. [PMID: 25307561 DOI: 10.1088/1741-2560/11/6/066003] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Neural prostheses, or brain-machine interfaces, aim to restore efficient communication and movement ability to those suffering from paralysis. A major challenge these systems face is robust performance, particularly with aging signal sources. The aim in this study was to develop a neural prosthesis that could sustain high performance in spite of signal instability while still minimizing retraining time. APPROACH We trained two rhesus macaques implanted with intracortical microelectrode arrays 1-4 years prior to this study to acquire targets with a neurally-controlled cursor. We measured their performance via achieved bitrate (bits per second, bps). This task was repeated over contiguous days to evaluate the sustained performance across time. MAIN RESULTS We found that in the monkey with a younger (i.e., two year old) implant and better signal quality, a fixed decoder could sustain performance for a month at a rate of 4 bps, the highest achieved communication rate reported to date. This fixed decoder was evaluated across 22 months and experienced a performance decline at a rate of 0.24 bps yr(-1). In the monkey with the older (i.e., 3.5 year old) implant and poorer signal quality, a fixed decoder could not sustain performance for more than a few days. Nevertheless, performance in this monkey was maintained for two weeks without requiring additional online retraining time by utilizing prior days' experimental data. Upon analysis of the changes in channel tuning, we found that this stability appeared partially attributable to the cancelling-out of neural tuning fluctuations when projected to two-dimensional cursor movements. SIGNIFICANCE The findings in this study (1) document the highest-performing communication neural prosthesis in monkeys, (2) confirm and extend prior reports of the stability of fixed decoders, and (3) demonstrate a protocol for system stability under conditions where fixed decoders would otherwise fail. These improvements to decoder stability are important for minimizing training time and should make neural prostheses more practical to use.
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Affiliation(s)
- Paul Nuyujukian
- Department of Bioengineering, Stanford University, Stanford, CA. School of Medicine, Stanford University, Stanford, CA. Department of Neurosurgery, Stanford University, Stanford, CA
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Fan JM, Nuyujukian P, Kao JC, Chestek CA, Ryu SI, Shenoy KV. Intention estimation in brain-machine interfaces. J Neural Eng 2014; 11:016004. [PMID: 24654266 DOI: 10.1088/1741-2560/11/1/016004] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
OBJECTIVE The objective of this work was to quantitatively investigate the mechanisms underlying the performance gains of the recently reported 'recalibrated feedback intention-trained Kalman Filter' (ReFIT-KF). APPROACH This was accomplished by designing variants of the ReFIT-KF algorithm and evaluating training and online data to understand the neural basis of this improvement. We focused on assessing the contribution of two training set innovations of the ReFIT-KF algorithm: intention estimation and the two-stage training paradigm. MAIN RESULTS Within the two-stage training paradigm, we found that intention estimation independently increased target acquisition rates by 37% and 59%, respectively, across two monkeys implanted with multiunit intracortical arrays. Intention estimation improved performance by enhancing the tuning properties and the mutual information between the kinematic and neural training data. Furthermore, intention estimation led to fewer shifts in channel tuning between the training set and online control, suggesting that less adaptation was required during online control. Retraining the decoder with online BMI training data also reduced shifts in tuning, suggesting a benefit of training a decoder in the same behavioral context; however, retraining also led to slower online decode velocities. Finally, we demonstrated that one- and two-stage training paradigms performed comparably when intention estimation is applied. SIGNIFICANCE These findings highlight the utility of intention estimation in reducing the need for adaptive strategies and improving the online performance of BMIs, helping to guide future BMI design decisions.
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Nuyujukian P, Fan JM, Kao JC, Ryu SI, Shenoy KV. A high-performance keyboard neural prosthesis enabled by task optimization. IEEE Trans Biomed Eng 2014; 62:21-29. [PMID: 25203982 DOI: 10.1109/tbme.2014.2354697] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Communication neural prostheses are an emerging class of medical devices that aim to restore efficient communication to people suffering from paralysis. These systems rely on an interface with the user, either via the use of a continuously moving cursor (e.g., mouse) or the discrete selection of symbols (e.g., keyboard). In developing these interfaces, many design choices have a significant impact on the performance of the system. The objective of this study was to explore the design choices of a continuously moving cursor neural prosthesis and optimize the interface to maximize information theoretic performance. We swept interface parameters of two keyboard-like tasks to find task and subject-specific optimal parameters as measured by achieved bitrate using two rhesus macaques implanted with multielectrode arrays. In this paper, we present the highest performing free-paced neural prosthesis under any recording modality with sustainable communication rates of up to 3.5 bits/s. These findings demonstrate that meaningful high performance can be achieved using an intracortical neural prosthesis, and that, when optimized, these systems may be appropriate for use as communication devices for those with physical disabilities.
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Affiliation(s)
- Paul Nuyujukian
- Bioengineering Department and School of Medicine and Neurosurgery Department, Stanford University, Stanford, CA, USA
| | - Joline M Fan
- Bioengineering Department and School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Jonathan C Kao
- Electrical Engineering Department, Stanford University, Stanford, CA, USA
| | - Stephen I Ryu
- Electrical Engineering Department, Stanford University, Stanford, CA, USA
| | - Krishna V Shenoy
- Bioengineering Department and Neurobiology Department and the Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA
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Orsborn AL, Carmena JM. Creating new functional circuits for action via brain-machine interfaces. Front Comput Neurosci 2013; 7:157. [PMID: 24204342 PMCID: PMC3817362 DOI: 10.3389/fncom.2013.00157] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2013] [Accepted: 10/20/2013] [Indexed: 11/29/2022] Open
Abstract
Brain-machine interfaces (BMIs) are an emerging technology with great promise for developing restorative therapies for those with disabilities. BMIs also create novel, well-defined functional circuits for action that are distinct from the natural sensorimotor apparatus. Closed-loop control of BMI systems can also actively engage learning and adaptation. These properties make BMIs uniquely suited to study learning of motor and non-physical, abstract skills. Recent work used motor BMIs to shed light on the neural representations of skill formation and motor adaptation. Emerging work in sensory BMIs, and other novel interface systems, also highlight the promise of using BMI systems to study fundamental questions in learning and sensorimotor control. This paper outlines the interpretation of BMIs as novel closed-loop systems and the benefits of these systems for studying learning. We review BMI learning studies, their relation to motor control, and propose future directions for this nascent field. Understanding learning in BMIs may both elucidate mechanisms of natural motor and abstract skill learning, and aid in developing the next generation of neuroprostheses.
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Affiliation(s)
- Amy L Orsborn
- 1UC Berkeley - UCSF Joint Graduate Program in Bioengineering, University of California Berkeley Berkeley, CA, USA
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Abstract
This essay summarizes recent advances in the field of brain-machine interfaces, with a focus on the learning and acquisition of neuroprosthetic skills. Significant progress has occurred in the field of brain–machine interfaces (BMI) since the first demonstrations with rodents, monkeys, and humans controlling different prosthetic devices directly with neural activity. This technology holds great potential to aid large numbers of people with neurological disorders. However, despite this initial enthusiasm and the plethora of available robotic technologies, existing neural interfaces cannot as yet master the control of prosthetic, paralyzed, or otherwise disabled limbs. Here I briefly discuss recent advances from our laboratory into the neural basis of BMIs that should lead to better prosthetic control and clinically viable solutions, as well as new insights into the neurobiology of action.
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Affiliation(s)
- Jose M Carmena
- Department of Electrical Engineering and Computer Sciences, Helen Wills Neuroscience Institute, University of California, Berkeley, California, United States of America.
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Orsborn AL, Dangi S, Moorman HG, Carmena JM. Closed-Loop Decoder Adaptation on Intermediate Time-Scales Facilitates Rapid BMI Performance Improvements Independent of Decoder Initialization Conditions. IEEE Trans Neural Syst Rehabil Eng 2012; 20:468-77. [DOI: 10.1109/tnsre.2012.2185066] [Citation(s) in RCA: 118] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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