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STUDY, MODEL & INTERFACE WITH MOTOR CORTEX
Presented by - Waseem Khatri
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Objective : Efficient decoding of neural information
for implementating neural motor prostheses
Motivation: Variety of Applications of Neural
Protheses Amputees can use artificial limbs Patients with Parkinsons disease Patients with paralysis/ spinal cord
injuries Epileptic seizures can be controlled
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Brain Anatomy
Regions of the Brain What controls the Motor Skills ? Discovery ! Where is the Motor cortex located ?
Area 6 is further divided into-Pre-motor area – Motion Control
- Supplementary motor area – Motion Planning
Source: Macgill University
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Homunculus
Little Man Somatotopic Representation Finest movements take more space Lips, Hands, Face have large areas in motor cortex
Source: http://thebrain.mcgill.ca/flash/a/a_06/a_06_cr/a_06_cr_mou/a_06_cr_mou.html
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Source: Mijail Surruya
Experimental Setup for Neural Prostheses
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Brain Controlled Vehicle for Paraplegic
Neural Interface Neural Signals
Sensors
Control Command
Vehicle State Signal
Vehicle
Environmental Feedback
Directional control
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Key Questions ?
Measurements -What can we measure? -From where ? -How ?
Encoding– How is the information represented in the
brain?
Decoding – What algorithms can we use to infer the
internal state of the brain ? Interface
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- Measurement
Source: Brown University
Why Primary Motor Cortex or M1 region ?• Firing rates of cells
correlated with hand motion (velocity, position, acceleration)
• Easily accessible
• Natural choice for controlling motion of a prosthetic device
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- Encoding TechniquesSome of the encoding techniques used are
Population Vector- Neurons in M1 are broadly tuned to the direction of hand
movement, with each neuron having a preferred direction of movement for which its firing rate is maximal
Linear Filtering- Cells in M1 encode muscle activity in a linear fashion
Artificial Neural Networks
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The Problem ?
Each of these methods estimates the hand kinematics x as a function of neural firing z
Encoding methods consider neural firing z as a function of hand kinematics x + noise.
But hand kinematics like – position, direction, velocity, acceleration etc. are considered in isolation
Hence , not very accurate results !Solution : Bayesian Population decoding using
Kalman filter
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The Experiment
•Pursuit Tracking Task
•Pinball Task
•Record the Neural Activity
•Record the Hand Kinematics
•Compute the posterior probability of the hand motion conditioned on a sequence of observed firing rates; this is formulated in terms of the product of likelihood and a prior
•The likelihood term models the probability of firing rates given a particular hand motion and can be learned from training data.
•The prior term defines a probabilistic model of hand kinematics
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Why Kalman Filter ?
Both the models : Likelihood and Prior are considered to be Gaussian.
A Kalman filter provides an efficient recursive method for Bayesian inference or estimating the posterior probability for the given two assumptions
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Pin Ball Task
Source: Brown University
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Generative Decoding Model
Source: Brown University
H is a matrix that linearly relates the six-dimensional hand state to the firing rates
A is the coefficient matrix
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Markov Assumptions
given the hand kinematics at time k-1, the hand kinematics at time k is conditionally independent of the previous hand motions
)/(),.....,/( 112,1 kkkkk xxpxxxxp
)/(),/( 1 kkkkk xzpzxzp
conditioned on the current state, the firing rates are independent of the firing rates at previous time instants
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Bayesian Inference
Source: Brown University
The posterior probability of the hand motion conditioned on a sequence of observed firing rates = The product of likelihood and a prior
Decoding involves estimating the posterior probability at each time instant
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Bayesian Decision based Classifier
To simulate decision process after decoding
Prosthetic Arm Motion Classes – Flex and Extention 2 sets of Neuron outputs Training Data Assumption: Gaussian Model
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Probability Density Functions
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Class PDF’s
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Classifier
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Thank You
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Questions ?