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Bhatt MW, Sharma S. Multi-scale self-attention approach for analysing motor imagery signals in brain-computer interfaces. J Neurosci Methods 2024; 408:110182. [PMID: 38795979 DOI: 10.1016/j.jneumeth.2024.110182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 05/01/2024] [Accepted: 05/22/2024] [Indexed: 05/28/2024]
Abstract
BACKGROUND Motor imagery-based electroencephalogram (EEG) brain-computer interface (BCI) technology has seen tremendous advancements in the past several years. Deep learning has outperformed more traditional approaches, such next-gen neuro-technologies, in terms of productivity. It is still challenging to develop and train an end-to-end network that can sufficiently extract the possible characteristics from EEG data used in motor imaging. Brain-computer interface research is largely reliant on the fundamental problem of accurately classifying EEG data. There are still many challenges in the field of MI classification even after researchers have proposed a variety of methods, such as deep learning and machine learning techniques. METHODOLOGY We provide a model for four-class categorization of motor imagery EEG signals using attention mechanisms: left hand, right hand, foot, and tongue/rest. The model is built on multi-scale spatiotemporal self-attention networks. To determine the most effective channels, self-attention networks are implemented spatially to assign greater weight to channels associated with motion and lesser weight to channels unrelated to motion. To eliminate noise in the temporal domain, parallel multi-scale Temporal Convolutional Network (TCN) layers are utilized to extract temporal domain features at various scales. RESULT On the IV-2b dataset from the BCI Competition, the suggested model achieved an accuracy of 85.09 %; on the IV-2a and IV-2b datasets from the HGD datasets, it was 96.26 %. COMPARISON WITH EXISTING METHODS In single-subject classification, this approach demonstrates superior accuracy when compared to existing methods. CONCLUSION The findings suggest that this approach exhibits commendable performance, resilience, and capacity for transfer learning.
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Affiliation(s)
- Mohammed Wasim Bhatt
- Department of Computer Science & Engineering, National Institute of Technology, Srinagar, J&K, India.
| | - Sparsh Sharma
- Department of Computer Science & Engineering, National Institute of Technology, Srinagar, J&K, India.
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Barnova K, Mikolasova M, Kahankova RV, Jaros R, Kawala-Sterniuk A, Snasel V, Mirjalili S, Pelc M, Martinek R. Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction. Comput Biol Med 2023; 163:107135. [PMID: 37329623 DOI: 10.1016/j.compbiomed.2023.107135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/13/2023] [Accepted: 06/04/2023] [Indexed: 06/19/2023]
Abstract
Brain-computer interfaces are used for direct two-way communication between the human brain and the computer. Brain signals contain valuable information about the mental state and brain activity of the examined subject. However, due to their non-stationarity and susceptibility to various types of interference, their processing, analysis and interpretation are challenging. For these reasons, the research in the field of brain-computer interfaces is focused on the implementation of artificial intelligence, especially in five main areas: calibration, noise suppression, communication, mental condition estimation, and motor imagery. The use of algorithms based on artificial intelligence and machine learning has proven to be very promising in these application domains, especially due to their ability to predict and learn from previous experience. Therefore, their implementation within medical technologies can contribute to more accurate information about the mental state of subjects, alleviate the consequences of serious diseases or improve the quality of life of disabled patients.
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Affiliation(s)
- Katerina Barnova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Martina Mikolasova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Radana Vilimkova Kahankova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia
| | - Rene Jaros
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland.
| | - Vaclav Snasel
- Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Australia.
| | - Mariusz Pelc
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland; School of Computing and Mathematical Sciences, University of Greenwich, London, UK.
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia; Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland.
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3
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Virtual Intelligence: A Systematic Review of the Development of Neural Networks in Brain Simulation Units. Brain Sci 2022; 12:brainsci12111552. [DOI: 10.3390/brainsci12111552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 10/18/2022] [Accepted: 10/26/2022] [Indexed: 11/17/2022] Open
Abstract
The functioning of the brain has been a complex and enigmatic phenomenon. From the first approaches made by Descartes about this organism as the vehicle of the mind to contemporary studies that consider the brain as an organism with emergent activities of primary and higher order, this organism has been the object of continuous exploration. It has been possible to develop a more profound study of brain functions through imaging techniques, the implementation of digital platforms or simulators through different programming languages and the use of multiple processors to emulate the speed at which synaptic processes are executed in the brain. The use of various computational architectures raises innumerable questions about the possible scope of disciplines such as computational neurosciences in the study of the brain and the possibility of deep knowledge into different devices with the support that information technology (IT) brings. One of the main interests of cognitive science is the opportunity to develop human intelligence in a system or mechanism. This paper takes the principal articles of three databases oriented to computational sciences (EbscoHost Web, IEEE Xplore and Compendex Engineering Village) to understand the current objectives of neural networks in studying the brain. The possible use of this kind of technology is to develop artificial intelligence (AI) systems that can replicate more complex human brain tasks (such as those involving consciousness). The results show the principal findings in research and topics in developing studies about neural networks in computational neurosciences. One of the principal developments is the use of neural networks as the basis of much computational architecture using multiple techniques such as computational neuromorphic chips, MRI images and brain–computer interfaces (BCI) to enhance the capacity to simulate brain activities. This article aims to review and analyze those studies carried out on the development of different computational architectures that focus on affecting various brain activities through neural networks. The aim is to determine the orientation and the main lines of research on this topic and work in routes that allow interdisciplinary collaboration.
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Wilson GH, Stavisky SD, Willett FR, Avansino DT, Kelemen JN, Hochberg LR, Henderson JM, Druckmann S, Shenoy KV. Decoding spoken English from intracortical electrode arrays in dorsal precentral gyrus. J Neural Eng 2020; 17:066007. [PMID: 33236720 PMCID: PMC8293867 DOI: 10.1088/1741-2552/abbfef] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
OBJECTIVE To evaluate the potential of intracortical electrode array signals for brain-computer interfaces (BCIs) to restore lost speech, we measured the performance of decoders trained to discriminate a comprehensive basis set of 39 English phonemes and to synthesize speech sounds via a neural pattern matching method. We decoded neural correlates of spoken-out-loud words in the 'hand knob' area of precentral gyrus, a step toward the eventual goal of decoding attempted speech from ventral speech areas in patients who are unable to speak. APPROACH Neural and audio data were recorded while two BrainGate2 pilot clinical trial participants, each with two chronically-implanted 96-electrode arrays, spoke 420 different words that broadly sampled English phonemes. Phoneme onsets were identified from audio recordings, and their identities were then classified from neural features consisting of each electrode's binned action potential counts or high-frequency local field potential power. Speech synthesis was performed using the 'Brain-to-Speech' pattern matching method. We also examined two potential confounds specific to decoding overt speech: acoustic contamination of neural signals and systematic differences in labeling different phonemes' onset times. MAIN RESULTS A linear decoder achieved up to 29.3% classification accuracy (chance = 6%) across 39 phonemes, while an RNN classifier achieved 33.9% accuracy. Parameter sweeps indicated that performance did not saturate when adding more electrodes or more training data, and that accuracy improved when utilizing time-varying structure in the data. Microphonic contamination and phoneme onset differences modestly increased decoding accuracy, but could be mitigated by acoustic artifact subtraction and using a neural speech onset marker, respectively. Speech synthesis achieved r = 0.523 correlation between true and reconstructed audio. SIGNIFICANCE The ability to decode speech using intracortical electrode array signals from a nontraditional speech area suggests that placing electrode arrays in ventral speech areas is a promising direction for speech BCIs.
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Affiliation(s)
- Guy H Wilson
- Neurosciences Graduate Program, Stanford University, Stanford, CA, United States of America
| | - Sergey D Stavisky
- Department of Neurosurgery, Stanford University, Stanford, CA, United States of America
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, United States of America
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America
| | - Francis R Willett
- Department of Neurosurgery, Stanford University, Stanford, CA, United States of America
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, United States of America
| | - Donald T Avansino
- Department of Neurosurgery, Stanford University, Stanford, CA, United States of America
| | - Jessica N Kelemen
- Department of Neurology, Harvard Medical School, Boston, MA, United States of America
| | - Leigh R Hochberg
- Department of Neurology, Harvard Medical School, Boston, MA, United States of America
- Center for Neurotechnology and Neurorecovery, Dept. of Neurology, Massachusetts General Hospital, Boston, MA, United States of America
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, United States of America
- Carney Institute for Brain Science and School of Engineering, Brown University, Providence, RI, United States of America
| | - Jaimie M Henderson
- Department of Neurosurgery, Stanford University, Stanford, CA, United States of America
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, United States of America
| | - Shaul Druckmann
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, United States of America
- Department of Neurobiology, Stanford University, Stanford, CA, United States of America
| | - Krishna V Shenoy
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, United States of America
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, United States of America
- Department of Neurobiology, Stanford University, Stanford, CA, United States of America
- Department of Bioengineering, Stanford University, Stanford, CA, United States of America
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5
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Even-Chen N, Muratore DG, Stavisky SD, Hochberg LR, Henderson JM, Murmann B, Shenoy KV. Power-saving design opportunities for wireless intracortical brain-computer interfaces. Nat Biomed Eng 2020; 4:984-996. [PMID: 32747834 PMCID: PMC8286886 DOI: 10.1038/s41551-020-0595-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 06/30/2020] [Indexed: 12/17/2022]
Abstract
The efficacy of wireless intracortical brain-computer interfaces (iBCIs) is limited in part by the number of recording channels, which is constrained by the power budget of the implantable system. Designing wireless iBCIs that provide the high-quality recordings of today's wired neural interfaces may lead to inadvertent over-design at the expense of power consumption and scalability. Here, we report analyses of neural signals collected from experimental iBCI measurements in rhesus macaques and from a clinical-trial participant with implanted 96-channel Utah multielectrode arrays to understand the trade-offs between signal quality and decoder performance. Moreover, we propose an efficient hardware design for clinically viable iBCIs, and suggest that the circuit design parameters of current recording iBCIs can be relaxed considerably without loss of performance. The proposed design may allow for an order-of-magnitude power savings and lead to clinically viable iBCIs with a higher channel count.
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Affiliation(s)
- Nir Even-Chen
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
| | - Dante G Muratore
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Sergey D Stavisky
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Leigh R Hochberg
- Department of Veterans Affairs Medical Center, Center for Neurorestoration and Neurotechnology, Providence, RI, USA
- School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jaimie M Henderson
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Boris Murmann
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- The Bio-X Institute, Stanford University, Stanford, CA, USA
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6
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Stavisky SD, Willett FR, Wilson GH, Murphy BA, Rezaii P, Avansino DT, Memberg WD, Miller JP, Kirsch RF, Hochberg LR, Ajiboye AB, Druckmann S, Shenoy KV, Henderson JM. Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis. eLife 2019; 8:e46015. [PMID: 31820736 PMCID: PMC6954053 DOI: 10.7554/elife.46015] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 11/14/2019] [Indexed: 01/20/2023] Open
Abstract
Speaking is a sensorimotor behavior whose neural basis is difficult to study with single neuron resolution due to the scarcity of human intracortical measurements. We used electrode arrays to record from the motor cortex 'hand knob' in two people with tetraplegia, an area not previously implicated in speech. Neurons modulated during speaking and during non-speaking movements of the tongue, lips, and jaw. This challenges whether the conventional model of a 'motor homunculus' division by major body regions extends to the single-neuron scale. Spoken words and syllables could be decoded from single trials, demonstrating the potential of intracortical recordings for brain-computer interfaces to restore speech. Two neural population dynamics features previously reported for arm movements were also present during speaking: a component that was mostly invariant across initiating different words, followed by rotatory dynamics during speaking. This suggests that common neural dynamical motifs may underlie movement of arm and speech articulators.
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Affiliation(s)
- Sergey D Stavisky
- Department of NeurosurgeryStanford UniversityStanfordUnited States
- Department of Electrical EngineeringStanford UniversityStanfordUnited States
| | - Francis R Willett
- Department of NeurosurgeryStanford UniversityStanfordUnited States
- Department of Electrical EngineeringStanford UniversityStanfordUnited States
| | - Guy H Wilson
- Neurosciences ProgramStanford UniversityStanfordUnited States
| | - Brian A Murphy
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandUnited States
- FES Center, Rehab R&D ServiceLouis Stokes Cleveland Department of Veterans Affairs Medical CenterClevelandUnited States
| | - Paymon Rezaii
- Department of NeurosurgeryStanford UniversityStanfordUnited States
| | | | - William D Memberg
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandUnited States
- FES Center, Rehab R&D ServiceLouis Stokes Cleveland Department of Veterans Affairs Medical CenterClevelandUnited States
| | - Jonathan P Miller
- FES Center, Rehab R&D ServiceLouis Stokes Cleveland Department of Veterans Affairs Medical CenterClevelandUnited States
- Department of NeurosurgeryUniversity Hospitals Cleveland Medical CenterClevelandUnited States
| | - Robert F Kirsch
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandUnited States
- FES Center, Rehab R&D ServiceLouis Stokes Cleveland Department of Veterans Affairs Medical CenterClevelandUnited States
| | - Leigh R Hochberg
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D ServiceProvidence VA Medical CenterProvidenceUnited States
- Center for Neurotechnology and Neurorecovery, Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonUnited States
- School of Engineering and Robert J. & Nandy D. Carney Institute for Brain ScienceBrown UniversityProvidenceUnited States
| | - A Bolu Ajiboye
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandUnited States
- FES Center, Rehab R&D ServiceLouis Stokes Cleveland Department of Veterans Affairs Medical CenterClevelandUnited States
| | - Shaul Druckmann
- Department of NeurobiologyStanford UniversityStanfordUnited States
| | - Krishna V Shenoy
- Department of Electrical EngineeringStanford UniversityStanfordUnited States
- Department of NeurobiologyStanford UniversityStanfordUnited States
- Department of BioengineeringStanford UniversityStanfordUnited States
- Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
- Wu Tsai Neurosciences InstituteStanford UniversityStanfordUnited States
- Bio-X ProgramStanford UniversityStanfordUnited States
| | - Jaimie M Henderson
- Department of NeurosurgeryStanford UniversityStanfordUnited States
- Wu Tsai Neurosciences InstituteStanford UniversityStanfordUnited States
- Bio-X ProgramStanford UniversityStanfordUnited States
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Bullard AJ, Hutchison BC, Lee J, Chestek CA, Patil PG. Estimating Risk for Future Intracranial, Fully Implanted, Modular Neuroprosthetic Systems: A Systematic Review of Hardware Complications in Clinical Deep Brain Stimulation and Experimental Human Intracortical Arrays. Neuromodulation 2019; 23:411-426. [DOI: 10.1111/ner.13069] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Revised: 08/05/2019] [Accepted: 09/10/2019] [Indexed: 01/08/2023]
Affiliation(s)
- Autumn J. Bullard
- Department of Biomedical Engineering University of Michigan Ann Arbor MI USA
| | | | - Jiseon Lee
- Department of Biomedical Engineering University of Michigan Ann Arbor MI USA
| | - Cynthia A. Chestek
- Department of Biomedical Engineering University of Michigan Ann Arbor MI USA
- Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA
| | - Parag G. Patil
- Department of Biomedical Engineering University of Michigan Ann Arbor MI USA
- Department of Neurosurgery University of Michigan Medical School Ann Arbor MI USA
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Stavisky SD, Kao JC, Nuyujukian P, Pandarinath C, Blabe C, Ryu SI, Hochberg LR, Henderson JM, Shenoy KV. Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements. Sci Rep 2018; 8:16357. [PMID: 30397281 PMCID: PMC6218537 DOI: 10.1038/s41598-018-34711-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 10/24/2018] [Indexed: 12/26/2022] Open
Abstract
Brain-machine interfaces (BMIs) that decode movement intentions should ignore neural modulation sources distinct from the intended command. However, neurophysiology and control theory suggest that motor cortex reflects the motor effector's position, which could be a nuisance variable. We investigated motor cortical correlates of BMI cursor position with or without concurrent arm movement. We show in two monkeys that subtracting away estimated neural correlates of position improves online BMI performance only if the animals were allowed to move their arm. To understand why, we compared the neural variance attributable to cursor position when the same task was performed using arm reaching, versus arms-restrained BMI use. Firing rates correlated with both BMI cursor and hand positions, but hand positional effects were greater. To examine whether BMI position influences decoding in people with paralysis, we analyzed data from two intracortical BMI clinical trial participants and performed an online decoder comparison in one participant. We found only small motor cortical correlates, which did not affect performance. These results suggest that arm movement and proprioception are the major contributors to position-related motor cortical correlates. Cursor position visual feedback is therefore unlikely to affect the performance of BMI-driven prosthetic systems being developed for people with paralysis.
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Affiliation(s)
- Sergey D Stavisky
- Neurosurgery Department, Stanford University, Stanford, CA, USA.
- Electrical Engineering Department, Stanford University, Stanford, CA, USA.
| | - Jonathan C Kao
- Electrical Engineering Department, Stanford University, Stanford, CA, USA
- Electrical and Computer Engineering Department, University of California at Los Angeles, Los Angeles, CA, USA
| | - Paul Nuyujukian
- Neurosurgery Department, Stanford University, Stanford, CA, USA
- Electrical Engineering Department, Stanford University, Stanford, CA, USA
- Bioengineering Department, Stanford University, Stanford, CA, USA
- Stanford Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Bio-X Program, Stanford University, Stanford, CA, USA
| | - Chethan Pandarinath
- Neurosurgery Department, Stanford University, Stanford, CA, USA
- Electrical Engineering Department, Stanford University, Stanford, CA, USA
| | - Christine Blabe
- Neurosurgery Department, Stanford University, Stanford, CA, USA
| | - Stephen I Ryu
- Electrical Engineering Department, Stanford University, Stanford, CA, USA
- Neurosurgery Department, Palo Alto Medical Foundation, Palo Alto, CA, USA
| | - Leigh R Hochberg
- Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, VA Medical Center, Providence, RI, USA
- School of Engineering and Carney Institute for Brain Science Brown University, Providence, RI, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Jaimie M Henderson
- Neurosurgery Department, Stanford University, Stanford, CA, USA
- Stanford Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Bio-X Program, Stanford University, Stanford, CA, USA
| | - Krishna V Shenoy
- Electrical Engineering Department, Stanford University, Stanford, CA, USA
- Bioengineering Department, Stanford University, Stanford, CA, USA
- Stanford Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Bio-X Program, Stanford University, Stanford, CA, USA
- Neurobiology Department, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
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