Bayesian population decoding of motor cortical activity using a Kalman filter,” (2006)

by W Wu, Y Gao, E Bienenstock, J P Donoghue, M J Black
Venue:Neural Computation,
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Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity

by Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, V. Shenoy, Maneesh Sahani, C. K. Machens, R. Romo, C. D. Brody, Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani - J Neurophysiol
"... You might find this additional information useful... A corrigendum for this article has been published. It can be found at: ..."
Abstract - Cited by 52 (15 self) - Add to MetaCart
You might find this additional information useful... A corrigendum for this article has been published. It can be found at:
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Reach and grasp by people with tetraplegia using a neurally controlled robotic arm.” Nature 485

by Leigh R. Hochberg, Daniel Bacher, Beata Jarosiewicz, Nicolas Y. Masse, John D, Joern Vogel, Sami Haddadin, Jie Liu, Sydney S. Cash, Patrick Van Der, John P. Donoghue - Donoghue , 2012
"... Paralysis following spinal cord injury (SCI), brainstem stroke, amyotrophic lateral sclerosis (ALS) and other disorders can disconnect the brain from the body, eliminating the ability to carry out volitional movements. A neural interface system (NIS) 1-5 could restore mobility and independence for p ..."
Abstract - Cited by 51 (2 self) - Add to MetaCart
Paralysis following spinal cord injury (SCI), brainstem stroke, amyotrophic lateral sclerosis (ALS) and other disorders can disconnect the brain from the body, eliminating the ability to carry out volitional movements. A neural interface system (NIS) 1-5 could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with longstanding tetraplegia can use an NIS to move and click a computer cursor and to control physical devices 6-8. Able-bodied monkeys have used an NIS to control a robotic arm 9, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here, we demonstrate the ability of two people with long-standing tetraplegia to use NIS-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96channel microelectrode array. One of the study participants, implanted with the sensor five years earlier, also used a robotic arm to drink coffee from a bottle. While robotic reach and

Empirical models of spiking in neural populations

by Jakob H. Macke, John P. Cunningham, Krishna V. Shenoy, Lars Büsing, Byron M. Yu, Maneesh Sahani
"... Neurons in the neocortex code and compute as part of a locally interconnected population. Large-scale multi-electrode recording makes it possible to access these population processes empirically by fitting statistical models to unaveraged data. What statistical structure best describes the concurren ..."
Abstract - Cited by 15 (13 self) - Add to MetaCart
Neurons in the neocortex code and compute as part of a locally interconnected population. Large-scale multi-electrode recording makes it possible to access these population processes empirically by fitting statistical models to unaveraged data. What statistical structure best describes the concurrent spiking of cells within a local network? We argue that in the cortex, where firing exhibits extensive correlations in both time and space and where a typical sample of neurons still reflects only a very small fraction of the local population, the most appropriate model captures shared variability by a low-dimensional latent process evolving with smooth dynamics, rather than by putative direct coupling. We test this claim by comparing a latent dynamical model with realistic spiking observations to coupled generalised linear spike-response models (GLMs) using cortical recordings. We find that the latent dynamical approach outperforms the GLM in terms of goodness-offit, and reproduces the temporal correlations in the data more accurately. We also compare models whose observations models are either derived from a Gaussian or point-process models, finding that the non-Gaussian model provides slightly better goodness-of-fit and more realistic population spike counts. 1
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GENERAL-PURPOSE FILTER DESIGN FOR NEURAL PROSTHETIC DEVICES LAKSHMINARAYAN SRINIVASAN

by Uri T. Eden, Sanjoy K. Mitter, Emery N. Brown, Page Srinivasan
"... Brain-driven interfaces depend on estimation procedures to convert neural signals to inputs for prosthetic devices that can assist individuals with severe motor deficits. Previous estimation procedures were developed on an application-specific basis. Here we report a coherent estimation framework th ..."
Abstract - Cited by 14 (2 self) - Add to MetaCart
Brain-driven interfaces depend on estimation procedures to convert neural signals to inputs for prosthetic devices that can assist individuals with severe motor deficits. Previous estimation procedures were developed on an application-specific basis. Here we report a coherent estimation framework that unifies these procedures and motivates new applications of prosthetic devices driven by action potentials, local field potentials (LFP), electrocorticography (ECoG), electroencephalography (EEG), electromyography (EMG), or optical methods. The brain-driven interface is described as a probabilistic relationship between neural activity and components of a prosthetic device that may take on discrete or continuous values. A new estimation procedure is developed for action potentials, and a corresponding procedure is described for field potentials and optical measurements.
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Neural Decoding of Hand Motion Using a Linear State-Space Model With Hidden States

by Wei Wu, Jayant E. Kulkarni, Nicholas G. Hatsopoulos, Liam Paninski
"... Abstract—The Kalman filter has been proposed as a model to decode neural activity measured from the motor cortex in order to obtain real-time estimates of hand motion in behavioral neurophysiological experiments. However, currently used linear state-space models underlying the Kalman filter do not t ..."
Abstract - Cited by 13 (1 self) - Add to MetaCart
Abstract—The Kalman filter has been proposed as a model to decode neural activity measured from the motor cortex in order to obtain real-time estimates of hand motion in behavioral neurophysiological experiments. However, currently used linear state-space models underlying the Kalman filter do not take into account other behavioral states such as muscular activity or the subject’s level of attention, which are often unobservable during experiments but may play important roles in characterizing neural controlled hand movement. To address this issue, we depict these unknown states as one multidimensional hidden state in the linear state-space framework. This new model assumes that the observed neural firing rate is directly related to this hidden state. The dynamics of the hand state are also allowed to impact the dynamics of the hidden state, and vice versa. The parameters in the model can be identified by a conventional expectation-maximization algorithm. Since this model still uses the linear Gaussian framework, hand-state decoding can be performed by the efficient Kalman filter algorithm. Experimental results show that this new model provides a more appropriate representation of the neural data and generates more accurate decoding. Furthermore, we have used recently developed computationally efficient methods by incorporating a priori information of the targets of the reaching movement. Our results show that the hidden-state model with target-conditioning further improves decoding accuracy. Index Terms—Hidden states, Kalman filter, motor cortex, neural decoding, state-space model.
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Real-time decoding of non-stationary neural activity in motor cortex

by Wei Wu, Nicholas G. Hatsopoulos - IEEE Trans. Neural Syst. Rehabil. Eng , 2008
"... Abstract—Neural decoding has played a key role in recent advances in brain–machine interfaces (BMIs) by converting brain signals into control commands to drive external devices such as robotic limbs or computer cursors. A number of practical algo-rithms including the well-known linear regression and ..."
Abstract - Cited by 12 (2 self) - Add to MetaCart
Abstract—Neural decoding has played a key role in recent advances in brain–machine interfaces (BMIs) by converting brain signals into control commands to drive external devices such as robotic limbs or computer cursors. A number of practical algo-rithms including the well-known linear regression and Kalman filter models have been used to predict continuous movement in a real-time online context using recordings from a chronically implanted multielectrode microarray in the motor cortex. Though effective, those models were often based on a strong assumption that the neural signal sequence is a stationary process. Recent work, however, indicates that the motor system significantly varies over time. To characterize the dynamic relationship between neural signals and hand kinematics, here we develop an adaptive approach for each of the linear regression and Kalman filter methods. Experimental results show that the new adaptive algo-rithms generate more accurate decoding than the nonadaptive algorithms. To make the new algorithms feasible in an online situation, we further develop a recursive update approach and theoretically demonstrate its superior efficiency. In particular, the adaptive Kalman filter is shown to be more accurate and efficient. We also test the new methods in a simulated BMI experiment where the true hand motion is not known. The successful perfor-mance suggests these methods could be useful decoding algorithms for practical applications. Index Terms—Adaptive models, brain–machine interfaces (BMIs), motor cortex, nonstationarity, real-time neural decoding. I.
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Neural Decoding of Movements: From Linear to Nonlinear Trajectory Models

by Byron M. Yu, John P. Cunningham, Krishna V. Shenoy, Maneesh Sahani
"... Abstract. To date, the neural decoding of time-evolving physical state – for example, the path of a foraging rat or arm movements – has been largely carried out using linear trajectory models, primarily due to their computational efficiency. The possibility of better capturing the statistics of the ..."
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Abstract. To date, the neural decoding of time-evolving physical state – for example, the path of a foraging rat or arm movements – has been largely carried out using linear trajectory models, primarily due to their computational efficiency. The possibility of better capturing the statistics of the movements using nonlinear trajectory models, thereby yielding more accurate decoded trajectories, is enticing. However, nonlinear decoding usually carries a higher computational cost, which is an important consideration in real-time settings. In this paper, we present techniques for nonlinear decoding employing modal Gaussian approximations, expectatation propagation, and Gaussian quadrature. We compare their decoding accuracy versus computation time tradeoffs based on high-dimensional simulated neural spike counts. Key words: Nonlinear dynamical models, nonlinear state estimation, neural decoding, neural prosthetics, expectation-propagation, Gaussian quadrature
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E.N.: A state-space framework for movement control to dynamic goals through brain-driven interfaces

by Lakshminarayan Srinivasan, Emery N. Brown, Senior Member - IEEE Trans. Biomed. Eng , 2007
"... Abstract—State-space estimation is a convenient framework for the design of brain-driven interfaces, where neural activity is used to control assistive devices for individuals with severe motor deficits. Recently, state-space approaches were developed to com-bine goal planning and trajectory-guiding ..."
Abstract - Cited by 9 (0 self) - Add to MetaCart
Abstract—State-space estimation is a convenient framework for the design of brain-driven interfaces, where neural activity is used to control assistive devices for individuals with severe motor deficits. Recently, state-space approaches were developed to com-bine goal planning and trajectory-guiding neural activity in the control of reaching movements of an assistive device to static goals. In this paper, we extend these algorithms to allow for goals that may change over the course of the reach. Performance between static and dynamic goal state equations and a standard free move-ment state equation is compared in simulation. Simulated trials are also used to explore the possibility of incorporating activity from parietal areas that have previously been associated with dy-namic goal position. Performance is quantified using mean-square error (MSE) of trajectory estimates. We also demonstrate the use of goal estimate MSE in evaluating algorithms for the control of goal-directed movements. Finally, we propose a framework to combine sensor data and control algorithms along with neural ac-tivity and state equations, to coordinate goal-directed movements through brain-driven interfaces. Index Terms—Goal-directed movement, neural prosthetic de-vice, recursive estimation, state equation.

Template-based spike pattern identification with linear convolution and dynamic time warping

by Zhiyi Chi, Wei Wu, Zach Haga, Nicholas G. Hatsopoulos, Daniel Margoliash, Zhiyi Chi, Wei Wu, Zach Haga, Nicholas G. Hatsopoulos, Daniel Margoliash - Journal of Neurophysiology , 2007
"... You might find this additional info useful... This article cites 45 articles, 20 of which you can access for free at: ..."
Abstract - Cited by 7 (2 self) - Add to MetaCart
You might find this additional info useful... This article cites 45 articles, 20 of which you can access for free at:

A monte carlo sequential estimation for point process optimum filtering

by Yiwen Wang, Student Member, António R. C. Paiva, Student Member, Joséc. Príncipe - Neural Networks, 2006. IJCNN ’06. International Joint Conference on, pp. 1846 – 1850 , 2006
"... Abstract — Adaptive filtering is normally utilized to estimate system states or outputs from continuous valued observations, and it is of limited use when the observations are discrete events. Recently a Bayesian approach to reconstruct the state from the discrete point observations has been propose ..."
Abstract - Cited by 6 (2 self) - Add to MetaCart
Abstract — Adaptive filtering is normally utilized to estimate system states or outputs from continuous valued observations, and it is of limited use when the observations are discrete events. Recently a Bayesian approach to reconstruct the state from the discrete point observations has been proposed. However, it assumes the posterior density of the state given the observations is Gaussian distributed, which is in general restrictive. We propose a Monte Carlo sequential estimation methodology to estimate directly the posterior density. Sample observations are generated at each time to recursively evaluate the posterior density more accurately. The state estimation is obtained easily by collapse, i.e. by smoothing the posterior density with Gaussian kernels to estimate its mean. The algorithm is tested in a simulated neural spike train decoding experiment and reconstructs better the velocity when compared with point process adaptive filtering algorithm with the Gaussian assumption. I.
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