STUDY, MODEL & INTERFACE WITH MOTOR CORTEX

Presented by - Waseem Khatri

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

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

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

Source: Mijail Surruya

Experimental Setup for Neural Prostheses

Brain Controlled Vehicle for Paraplegic

Neural Interface Neural Signals

Sensors

Control Command

Vehicle State Signal

Vehicle

Environmental Feedback

Directional control

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

- 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

- 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

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

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

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

Pin Ball Task

Source: Brown University

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

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

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

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

Probability Density Functions

Class PDF’s

Classifier

Thank You

Questions ?