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Losanno E, Badi M, Roussinova E, Bogaard A, Delacombaz M, Shokur S, Micera S. An Investigation of Manifold-Based Direct Control for a Brain-to-Body Neural Bypass. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:271-280. [PMID: 38766541 PMCID: PMC11100864 DOI: 10.1109/ojemb.2024.3381475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 02/06/2024] [Accepted: 03/11/2024] [Indexed: 05/22/2024] Open
Abstract
Objective: Brain-body interfaces (BBIs) have emerged as a very promising solution for restoring voluntary hand control in people with upper-limb paralysis. The BBI module decoding motor commands from brain signals should provide the user with intuitive, accurate, and stable control. Here, we present a preliminary investigation in a monkey of a brain decoding strategy based on the direct coupling between the activity of intrinsic neural ensembles and output variables, aiming at achieving ease of learning and long-term robustness. Results: We identified an intrinsic low-dimensional space (called manifold) capturing the co-variation patterns of the monkey's neural activity associated to reach-to-grasp movements. We then tested the animal's ability to directly control a computer cursor using cortical activation along the manifold axes. By daily recalibrating only scaling factors, we achieved rapid learning and stable high performance in simple, incremental 2D tasks over more than 12 weeks of experiments. Finally, we showed that this brain decoding strategy can be effectively coupled to peripheral nerve stimulation to trigger voluntary hand movements. Conclusions: These results represent a proof of concept of manifold-based direct control for BBI applications.
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Affiliation(s)
- E. Losanno
- The Biorobotics Institute and Department of Excellence in Robotics and AIScuola Superiore Sant'Anna56025PisaItaly
- Modular Implantable Neuroprostheses (MINE) LaboratoryUniversità Vita-Salute San Raffaele and Scuola Superiore Sant'AnnaMilanItaly
| | - M. Badi
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of BioengineeringÉcole Polytechnique Fédérale de Lausanne (EPFL)1015LausanneSwitzerland
| | - E. Roussinova
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of BioengineeringÉcole Polytechnique Fédérale de Lausanne (EPFL)1015LausanneSwitzerland
| | - A. Bogaard
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and MedicineUniversity of Fribourg1700FribourgSwitzerland
| | - M. Delacombaz
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and MedicineUniversity of Fribourg1700FribourgSwitzerland
| | - S. Shokur
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of BioengineeringÉcole Polytechnique Fédérale de Lausanne (EPFL)1015LausanneSwitzerland
| | - S. Micera
- The Biorobotics Institute and Department of Excellence in Robotics and AIScuola Superiore Sant'Anna56025PisaItaly
- Modular Implantable Neuroprostheses (MINE) LaboratoryUniversità Vita-Salute San Raffaele and Scuola Superiore Sant'AnnaMilanItaly
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of BioengineeringÉcole Polytechnique Fédérale de Lausanne (EPFL)1015LausanneSwitzerland
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Oby ER, Degenhart AD, Grigsby EM, Motiwala A, McClain NT, Marino PJ, Yu BM, Batista AP. Dynamical constraints on neural population activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.03.573543. [PMID: 38260549 PMCID: PMC10802336 DOI: 10.1101/2024.01.03.573543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The manner in which neural activity unfolds over time is thought to be central to sensory, motor, and cognitive functions in the brain. Network models have long posited that the brain's computations involve time courses of activity that are shaped by the underlying network. A prediction from this view is that the activity time courses should be difficult to violate. We leveraged a brain-computer interface (BCI) to challenge monkeys to violate the naturally-occurring time courses of neural population activity that we observed in motor cortex. This included challenging animals to traverse the natural time course of neural activity in a time-reversed manner. Animals were unable to violate the natural time courses of neural activity when directly challenged to do so. These results provide empirical support for the view that activity time courses observed in the brain indeed reflect the underlying network-level computational mechanisms that they are believed to implement.
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Abstract
Choking under pressure is a frustrating phenomenon experienced sometimes by skilled performers as well as during everyday life. The phenomenon has been extensively studied in humans, but it has not been previously shown whether animals also choke under pressure. Here we report that rhesus monkeys also choke under pressure. This indicates that there may be shared neural mechanisms that underlie the behavior in both humans and monkeys. Introducing an animal model for choking under pressure allows for opportunities to study the neural causes of this paradoxical behavior. In high-stakes situations, people sometimes exhibit a frustrating phenomenon known as “choking under pressure.” Usually, we perform better when the potential payoff is larger. However, once potential rewards get too high, performance paradoxically decreases—we “choke.” Why do we choke under pressure? An animal model of choking would facilitate the investigation of its neural basis. However, it could be that choking is a uniquely human occurrence. To determine whether animals also choke, we trained three rhesus monkeys to perform a difficult reaching task in which they knew in advance the amount of reward to be given upon successful completion. Like humans, monkeys performed worse when potential rewards were exceptionally valuable. Failures that occurred at the highest level of reward were due to overly cautious reaching, in line with the psychological theory that explicit monitoring of behavior leads to choking. Our results demonstrate that choking under pressure is not unique to humans, and thus, its neural basis might be conserved across species.
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Farrokhi B, Erfanian A. A state-based probabilistic method for decoding hand position during movement from ECoG signals in non-human primate. J Neural Eng 2020; 17:026042. [PMID: 32224511 DOI: 10.1088/1741-2552/ab848b] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVE In this study, we proposed a state-based probabilistic method for decoding hand positions during unilateral and bilateral movements using the ECoG signals recorded from the brain of Rhesus monkey. APPROACH A customized electrode array was implanted subdurally in the right hemisphere of the brain covering from the primary motor cortex to the frontal cortex. Three different experimental paradigms were considered: ipsilateral, contralateral, and bilateral movements. During unilateral movement, the monkey was trained to get food with one hand, while during bilateral movement, the monkey used its left and right hands alternately to get food. To estimate the hand positions, a state-based probabilistic method was introduced which was based on the conditional probability of the hand movement state (i.e. idle, right hand movement, and left hand movement) and the conditional expectation of the hand position for each state. Moreover, a hybrid feature extraction method based on linear discriminant analysis and partial least squares (PLS) was introduced. MAIN RESULTS The proposed method could successfully decode the hand positions during ipsilateral, contralateral, and bilateral movements and significantly improved the decoding performance compared to the conventional Kalman and PLS regression methods [Formula: see text]. The proposed hybrid feature extraction method was found to outperform both the PLS and PCA methods [Formula: see text]. Investigating the kinematic information of each frequency band shows that more informative frequency bands were [Formula: see text] (15-30 Hz) and [Formula: see text](50-100 Hz) for ipsilateral and [Formula: see text] and [Formula: see text] (100-200 Hz) for contralateral movements. It is observed that ipsilateral movement was decoded better than contralateral movement for [Formula: see text] (5-15 Hz) and [Formula: see text] bands, while contralateral movements was decoded better for [Formula: see text] (30-200 Hz) and hfECoG (200-400 Hz) bands. SIGNIFICANCE Accurate decoding the bilateral movement using the ECoG recorded from one brain hemisphere is an important issue toward real-life applications of the brain-machine interface technologies.
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Affiliation(s)
- Behraz Farrokhi
- Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Iran Neural Technology Research Centre, Tehran, Iran
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Issar D, Williamson RC, Khanna SB, Smith MA. A neural network for online spike classification that improves decoding accuracy. J Neurophysiol 2020; 123:1472-1485. [PMID: 32101491 PMCID: PMC7191521 DOI: 10.1152/jn.00641.2019] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 02/26/2020] [Accepted: 02/26/2020] [Indexed: 11/22/2022] Open
Abstract
Separating neural signals from noise can improve brain-computer interface performance and stability. However, most algorithms for separating neural action potentials from noise are not suitable for use in real time and have shown mixed effects on decoding performance. With the goal of removing noise that impedes online decoding, we sought to automate the intuition of human spike-sorters to operate in real time with an easily tunable parameter governing the stringency with which spike waveforms are classified. We trained an artificial neural network with one hidden layer on neural waveforms that were hand-labeled as either spikes or noise. The network output was a likelihood metric for each waveform it classified, and we tuned the network's stringency by varying the minimum likelihood value for a waveform to be considered a spike. Using the network's labels to exclude noise waveforms, we decoded remembered target location during a memory-guided saccade task from electrode arrays implanted in prefrontal cortex of rhesus macaque monkeys. The network classified waveforms in real time, and its classifications were qualitatively similar to those of a human spike-sorter. Compared with decoding with threshold crossings, in most sessions we improved decoding performance by removing waveforms with low spike likelihood values. Furthermore, decoding with our network's classifications became more beneficial as time since array implantation increased. Our classifier serves as a feasible preprocessing step, with little risk of harm, that could be applied to both off-line neural data analyses and online decoding.NEW & NOTEWORTHY Although there are many spike-sorting methods that isolate well-defined single units, these methods typically involve human intervention and have inconsistent effects on decoding. We used human classified neural waveforms as training data to create an artificial neural network that could be tuned to separate spikes from noise that impaired decoding. We found that this network operated in real time and was suitable for both off-line data processing and online decoding.
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Affiliation(s)
- Deepa Issar
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania
- University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Ryan C Williamson
- University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Machine Learning, Carnegie Mellon University, Pittsburgh, Pennsylvania
- Carnegie Mellon Neuroscience Institute, Pittsburgh, Pennsylvania
| | - Sanjeev B Khanna
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Matthew A Smith
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania
- Carnegie Mellon Neuroscience Institute, Pittsburgh, Pennsylvania
- Department of Ophthalmology, University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
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Chen J, Hao Y, Zhang S, Sun G, Xu K, Chen W, Zheng X. An automated behavioral apparatus to combine parameterized reaching and grasping movements in 3D space. J Neurosci Methods 2018; 312:139-147. [PMID: 30502371 DOI: 10.1016/j.jneumeth.2018.11.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 11/27/2018] [Accepted: 11/27/2018] [Indexed: 01/17/2023]
Abstract
BACKGROUND The neural principles underlying reaching and grasping movements have been studied extensively in primates for decades. However, few experimental apparatuses have been developed to enable a flexible combination of reaching and grasping in one task in three-dimensional (3D) space. NEW METHOD By combining a custom turning table with a 3D translational device, we have developed a highly flexible apparatus that enables the subject to reach multiple positions in 3D space, and grasp differently shaped objects with multiple grip types in each position. Meanwhile, hand trajectory and grip types can be recorded via optical motion tracking cameras and touch sensors, respectively. RESULTS We have used the apparatus to successfully train a macaque monkey to accomplish a visually-guided reach-to-grasp task, in which, six objects, fixed on the turning table, were grasped appropriately when they were transported to multiple positions in 3D space. A preliminary analysis of neural signals recorded in primary motor cortex, shows that plenty of neurons exhibit significant tuning to both target position and grip type. COMPARISON WITH EXISTING METHOD(S) Our apparatus realizes an arbitrary combination of parameterized reaching and grasping movements in a single task, which were usually separated or fixed in other systems. Meanwhile, the apparatus has high expansibility in terms of dynamic range, object shapes and applicable subjects. CONCLUSIONS The apparatus provides a valuable platform to study upper limb functions in behavioral and neurophysiological studies, and may facilitate simultaneous reconstruction of reaching and grasping movements in brain-machine interfaces (BMIs).
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Affiliation(s)
- Junjun Chen
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, 310027, China; Department of Biomedical Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Yaoyao Hao
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, 310027, China; Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310027, China.
| | - Shaomin Zhang
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, 310027, China; Department of Biomedical Engineering, Zhejiang University, Hangzhou, 310027, China; Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310027, China
| | - Guanghao Sun
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, 310027, China; Department of Biomedical Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Kedi Xu
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, 310027, China; Department of Biomedical Engineering, Zhejiang University, Hangzhou, 310027, China; Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310027, China
| | - Weidong Chen
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, 310027, China; Department of Biomedical Engineering, Zhejiang University, Hangzhou, 310027, China; Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310027, China
| | - Xiaoxiang Zheng
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, 310027, China; Department of Biomedical Engineering, Zhejiang University, Hangzhou, 310027, China; Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310027, China
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Li H, Hao Y, Zhang S, Wang Y, Chen W, Zheng X. Prior Knowledge of Target Direction and Intended Movement Selection Improves Indirect Reaching Movement Decoding. Behav Neurol 2017; 2017:2182843. [PMID: 28490836 PMCID: PMC5406739 DOI: 10.1155/2017/2182843] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Revised: 02/21/2017] [Accepted: 03/02/2017] [Indexed: 11/17/2022] Open
Abstract
Objective. Previous studies have demonstrated that target direction information presented by the dorsal premotor cortex (PMd) during movement planning could be incorporated into neural decoder for achieving better decoding performance. It is still unknown whether the neural decoder combined with only target direction could work in more complex tasks where obstacles impeded direct reaching paths. Methods. In this study, spike activities were collected from the PMd of two monkeys when performing a delayed obstacle-avoidance task. We examined how target direction and intended movement selection were encoded in neuron population activities of the PMd during movement planning. The decoding performances of movement trajectory were compared for three neural decoders with no prior knowledge, or only target direction, or both target direction and intended movement selection integrated into a mixture of trajectory model (MTM). Results. We found that not only target direction but also intended movement selection was presented in neural activities of the PMd during movement planning. It was further confirmed by quantitative analysis. Combined with prior knowledge, the trajectory decoder achieved the best performance among three decoders. Conclusion. Recruiting prior knowledge about target direction and intended movement selection extracted from the PMd could enhance the decoding performance of hand trajectory in indirect reaching movement.
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Affiliation(s)
- Hongbao Li
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China
- Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Hangzhou 310027, China
| | - Yaoyao Hao
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China
- Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
| | - Shaomin Zhang
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China
- Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Hangzhou 310027, China
| | - Yiwen Wang
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China
| | - Weidong Chen
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China
| | - Xiaoxiang Zheng
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China
- Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China
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Shanechi MM, Orsborn AL, Moorman HG, Gowda S, Dangi S, Carmena JM. Rapid control and feedback rates enhance neuroprosthetic control. Nat Commun 2017; 8:13825. [PMID: 28059065 PMCID: PMC5227098 DOI: 10.1038/ncomms13825] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2016] [Accepted: 11/03/2016] [Indexed: 12/20/2022] Open
Abstract
Brain-machine interfaces (BMI) create novel sensorimotor pathways for action. Much as the sensorimotor apparatus shapes natural motor control, the BMI pathway characteristics may also influence neuroprosthetic control. Here, we explore the influence of control and feedback rates, where control rate indicates how often motor commands are sent from the brain to the prosthetic, and feedback rate indicates how often visual feedback of the prosthetic is provided to the subject. We developed a new BMI that allows arbitrarily fast control and feedback rates, and used it to dissociate the effects of each rate in two monkeys. Increasing the control rate significantly improved control even when feedback rate was unchanged. Increasing the feedback rate further facilitated control. We also show that our high-rate BMI significantly outperformed state-of-the-art methods due to higher control and feedback rates, combined with a different point process mathematical encoding model. Our BMI paradigm can dissect the contribution of different elements in the sensorimotor pathway, providing a unique tool for studying neuroprosthetic control mechanisms.
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Affiliation(s)
- Maryam M. Shanechi
- Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California 90089, USA
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, California 94720, USA
| | - Amy L. Orsborn
- UC Berkeley — UCSF Joint Graduate Program in Bioengineering, University of California, Berkeley, Berkeley, California 94720, USA
| | - Helene G. Moorman
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California 94720, USA
| | - Suraj Gowda
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, California 94720, USA
| | - Siddharth Dangi
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, California 94720, USA
| | - Jose M. Carmena
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, California 94720, USA
- UC Berkeley — UCSF Joint Graduate Program in Bioengineering, University of California, Berkeley, Berkeley, California 94720, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California 94720, USA
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