1. STUDY, MODEL & INTERFACE WITH MOTOR CORTEX Presented by - Waseem Khatri

2. 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

3. 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

4. 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

5. Source: Mijail Surruya Experimental Setup for Neural Prostheses

6. Brain Controlled Vehicle for Paraplegic Neural Interface Neural Signals Sensors Control Command Vehicle State Signal Vehicle Environmental Feedback Directional control

7. 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

8. - 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

9. - Encoding Techniques Some 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

10. 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

11. 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

12. 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

13. Pin Ball Task Source: Brown University

14. 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

15. Markov Assumptions given the hand kinematics at time k-1, the hand kinematics at time k is conditionally independent of the previous hand motions conditioned on the current state, the firing rates are independent of the firing rates at previous time instants

16. 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

17. 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

18. Probability Density Functions

19. Class PDF’s

20. Classifier

21. Thank You

22. Questions ?