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TOWAD BRAIN-COMPUTER INTERFACING Beong Wook Yoo CONTENTS 9. Probabilistically Modeling and Decoding Neral Population Activity in Motor Cortex 10. The Importance of Online Error Correction and Feed-Forward Adjustments in Brain-Machine Interfaces for Restoration of Movement CHAPTER. 9 Abstract Introduction Sensing Neural Activity Encoding Decoding Interfaces Discussion and Conclusion ABSTRACT Recent work on probabilistic models of motor cortical activity and methods for inferring, or decoding, hand movements from this activity Bayesian approach Kalman filter INTRODUCTION Two fundamental shifts in neuroscinence - new eletrode array technology - study of more natural stimuli and behaviors observe large populations of cortical cells and how they respond during rich behavioral tasks. Probabilistic model relating high dimensional signals INTRODUCTION Sequence of firing rates Zt = [ zt … z1 ], zt = [ z1,t … zn,t ] Hand movements Xt = [ xt … x1 ], xt = [x1,t … xn,t ] INTRODUCTION Standard models of motor cortical tuning General linear model relating hand motion and neural firing rates Decoding algorithm emerges based on Bayesian probability Applications to neural prostheses SENSING NEURAL ACTIVITY Recording of neural activity with varying levels of temporal and spatial resolution Microelectrode array SENSING NEURAL ACTIVITY Constraints of implant area - using region of the brain to control movement - accessibility of this region Detecting action potential using manual and automatic spike sorting techniques 2D cursor control task ENCODING Generative model of neural activity zt : firing activity xt : hand kinematics qt : noise vector - descriptive rather than mechanistic ENCODING With the generative approach, the problem of modeling the neural code has four components (1) What neural data should be modeled? (2) What behavioral variables are important? (3) What functional relationship between behavior and neural activity is appropriate? (4) What model of “noise” should be used? ENCODING (1) firing rates computed from spike counts (2) including limb joint angles, torques, or muscle activity (3) f could be an arbitrary function - low dimensional, linear parametric model ENCODING Motor cortical model - directionally tuned - speed - position, acceleration and higher order derivatives ENCODING Firing rates are approximated as a linear combination of simple hand kinematics is a vector of n cells’ firing rates (4) first firing rates are strictly positive and over relatively small, exhibit a roughly Poisson distribution → but, prefer to model the noise as Gaussian(9.5) → qt~ N(0,Q) DECODING Probabilistically, we would like to represent the a posteriori probability of the hand motion p(xt|Zt) Assumptions - the hand kinematics at time t are independent of those at time t-2 and earlier conditioned on xt-1 - the firing rates at time t are conditionally independent of the hand kinematics at earlier times With these assumptions, Bayes’ rule can be used to derive an expression for the posterior probability in terms of the likelihood and the prior DECODING Bayes’ rule Reculsive equation DECODING Prior probability of hand motions in our task is well approximated by a linear Gaussian model A is state matrix With these assumptions, the likelihood term Using Kalman filter Using fixed lag to improve decoding accuracy INTERFACES In the case of cortical implants, recording technologies and different decoding algorithms can be classified according to two kinds of interfaces - discrete, continuous In the discrete task, a monkey has one of a fixed number of target they must select by either direct arm motion or neural signals. Interfaces based on selection of a small number of states can be cumbersome to use. INTERFACES Closed-loop control task Flexible and noise-prone Combining discrete and continuous control Switching filter DISCUSSION AND CONCLUSIONS Efficient learning and decoding methods do not currently exist for non-Gaussian, nonlinear models of point processes Develop new machine learning methods for capturing the high dimensional relationship btw motor behavior and neural firing Additional information may be obtained from premotor and parietal areas Training data for the encoding model Moving beyond simple 2D or 3D cursor control to ultimately give patients high-dimensional control of devices such as dexterous robot hands CHAPTER. 10 Abstract Introduction Decoder Limitations in BCIs/BMIs Feed-Forward Adjustment to Imperfect Decoders Real-Time Adjustments to Random Variability in the Motor System and in the Assistive Device Implications for Restoration of Arm and Hand Function ABSTRACT The microelectrode arrays can record only a small fraction of the neurons We are unable to decode the user’s desired movement without errors Consistent and random error Feed-forward adjustments Online error corrections INTRODUCTION BCIs and BMIs have the potential to help people with severe motor disabilities by enabling them to control various devices directly with their neural activity Systems used neural signals involved with sensorimotor processing that accompany imagined or attempted movements of paralyzed limbs are most useful for individuals - the sensorimotor-related brain areas are still intact - the command signals needed by the assistive device are movement-related INTRODUCTION One promising use of these brain-derived movement commands is in restoring control of arm and hand function to people with high-level spinal cord injuries. Use of recorded brain activity is a viable option for command of these more complex upper-limb functional electrical stimulation(FES) systems Evidence in three forms - ability to adjust and correct for consistent errors - ability to make online corrections to random errors - ability to increase the useful information content of the recorded neural signals DECODER LIMITATIONS IN BCIS/BMIS Microelectrode arrays detecting activity of neurons - detecting small fraction of the neurons EEGs or ECoGs detecting field potentials Imperfect decoders result in two types of errors - consistent errors - random errors FEED-FORWARD ADJUSTMENT TO IMPERFECT DECODERS Both humans and nonhuman learn to make feedforward modifications to their motor output to correct for these errors Visual feedback enables users to identify these consistent decoding and device errors and then compensate for the errors by modifying their motor plan FEED-FORWARD ADJUSTMENT TO IMPERFECT DECODERS 3D center-out movement task Switching from the actual wrist position to the predicted wrist position FEED-FORWARD ADJUSTMENT TO IMPERFECT DECODERS Population vector Neural firing rate linearly related to intended movement direction Neural firing rate are related to movement speed FEED-FORWARD ADJUSTMENT TO IMPERFECT DECODERS Learning to make feed-forward corrections in its motor output Problem in experiment REAL-TIME ADJUSTMENTS TO RANDOM VARIABILITY IN THE MOTOR SYSTEM AND IN THE ASSISTIVE DEVICE Random errors - the stochastic nature of neural processing - our limited ability to access the firing activity of the full neural ensemble - assistive device FES system REAL-TIME ADJUSTMENTS TO RANDOM VARIABILITY 3D movements of a robotic arm in space Additional consistent and random error of the robot Training an animal - Initial attempts - viewing the activities in the familiar virtual environment IMPLICATIONS FOR RESTORATION OF ARM AND HAND FUNCTION Visual feedback Improving BMI function - Perceiving somatosensory information - Conveying finely graded continuous movement information Non-brain based means Reinstating practice Thank you!