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TOWAD BRAIN-COMPUTER
INTERFACING
Beong Wook Yoo
CONTENTS
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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
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Abstract
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Introduction
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Sensing Neural Activity
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Encoding
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Decoding
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Interfaces
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Discussion and Conclusion
ABSTRACT
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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
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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
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Sequence of firing rates
Zt = [ zt … z1 ], zt = [ z1,t … zn,t ]
Hand movements
Xt = [ xt … x1 ], xt = [x1,t … xn,t ]
INTRODUCTION
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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
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Recording of neural activity with varying levels of
temporal and spatial resolution
Microelectrode array
SENSING NEURAL ACTIVITY
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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
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Generative model of neural activity
zt : firing activity
xt : hand kinematics
qt : noise vector
- descriptive rather than mechanistic
ENCODING
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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
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(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
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Motor cortical model
- directionally tuned
- speed
- position, acceleration and higher order derivatives
ENCODING
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Firing rates are approximated as a linear combination
of simple hand kinematics
is a vector of n cells’ firing rates
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(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
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- the firing rates at time t are conditionally independent
of the hand kinematics at earlier times
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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
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Bayes’ rule
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Reculsive equation
DECODING
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Prior probability of hand motions in our task is well
approximated by a linear Gaussian model
A is state matrix
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With these assumptions, the likelihood term
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Using Kalman filter
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Using fixed lag to improve decoding accuracy
INTERFACES
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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
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Closed-loop control task
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Flexible and noise-prone
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Combining discrete and continuous control
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Switching filter
DISCUSSION AND CONCLUSIONS
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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
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Abstract

Introduction
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Decoder Limitations in BCIs/BMIs
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Feed-Forward Adjustment to Imperfect Decoders
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Real-Time Adjustments to Random Variability in the
Motor System and in the Assistive Device
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Implications for Restoration of Arm and Hand
Function
ABSTRACT
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The microelectrode arrays can record only a small
fraction of the neurons
We are unable to decode the user’s desired movement
without errors
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Consistent and random error
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Feed-forward adjustments
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Online error corrections
INTRODUCTION
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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
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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
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Microelectrode arrays detecting activity of
neurons
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- detecting small fraction of the neurons
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EEGs or ECoGs detecting field potentials
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Imperfect decoders result in two types of errors
- consistent errors
- random errors
FEED-FORWARD ADJUSTMENT TO
IMPERFECT DECODERS
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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
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3D center-out movement task
Switching from the actual wrist position to the
predicted wrist position
FEED-FORWARD ADJUSTMENT TO
IMPERFECT DECODERS
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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
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REAL-TIME ADJUSTMENTS TO RANDOM
VARIABILITY IN THE MOTOR SYSTEM AND IN
THE ASSISTIVE DEVICE
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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
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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
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Visual feedback
Improving BMI function
- Perceiving somatosensory information
- Conveying finely graded continuous movement
information
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Non-brain based means
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Reinstating practice
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