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BMI Principles Jose C. Principe University of Florida Adapted from Hayrettin Grkk, U. of Twente, NL

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Literature

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Difficulties in Invasive BMIs BCIs offer an easy entry to research Non invasiveness straight forward data collection Closer to cognition Conventional signal processing BMIs research infrastructure is much harder Work with animals (ethics) Difficult instrumentation Unclear signal processing

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Choice of Scale for Neuroprosthetics Bandwidth (approximate) Localization Scalp Electrodes 0 ~ 80 HzCortical Surface Volume Conduction 3-5 cm Electro- corticogram (ECoG) 0 ~ 500HzCortical Surface 0.5-1 cm Micro Electrodes 0 ~ 500Hz 500 ~ 7kHz Local Fields 1mm Single Neuron 200 m

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Electrode Arrays J. C. Sanchez, N. Alba, T. Nishida, C. Batich, and P. R. Carney, "Structural modifications in chronic microwire electrodes for cortical neuroprosthetics: a case study," IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006 Utah array Brain Gate Michigan probes

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Technical Issues with BMIs An implantable BMI requires beyond of state of the art technology: Ultra low power Ultra miniaturized Huge data bandwidth/power form factor Packaging

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28mm 15mm 12mm Thru vias to RX/Power Coil + 12.5 mm Coil winding 3.5 mm 50m pitch Electrodes Coin Battery (10 x 2.5 mm) Thru vias to Battery Supporting screws Flexible substrate TX antenna Modular Electrodes Electrode attachment sites IF-IC RFIC 18 mm Coil Battery Patterned Substrate Supporting Substrate Electrode Array IC Flip-chip connection Specifications: 16 flexible microelectrodes (40 dB, 20 KHz) Wireless (500 Kpulse/sec) 2mW of power (72-96 hours between charges) FWIRE: Florida Wireless Implantable Recording Electrodes

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RatPack Low-Power, Wireless, Portable BMIs Requirements Total Weight: < 100g Battery Powered: Run for 4 hours Implantable Biocompatible Heat flux: < 50 mW/cm 2 Power dissipation should not exceed a few hundred milliwatts Backpack Small form factor Speed vs. Low Power

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UF PICO System PICO system = DSP + Wireless Generation 3

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J.R. Wolpaw et al. 2002 BCI (BMI) bypasses the brains normal pathways of peripheral nerves (and muscles) General Architecture

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BMIs: How to put it together? NeoCortical Brain Areas Related to Movement Posterior Parietal (PP) Visual to motor transformation Premotor (PM) and Dorsal Premotor (PMD) - Planning and guidance (visual inputs) Primary Motor (M1) Initiates muscle contraction

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Motor Tasks Performed Task 1 Task 2 Data 2 Owl monkeys Belle, Carmen 2 Rhesus monkeys Aurora, Ivy 54-192 sorted cells Cortices sampled: PP, M1, PMd, S1, SMA Neuronal rate (100 Hz) and behavior is time synchronized and downsampled to 10Hz

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100 msec Binned Counts Raster of 105 neurons (spike sorted)

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Ensemble Correlations Local in Time are Averaged with Global Models

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Computational Models of Neural Intent Three different levels of neurophysiology realism Black Box models function relation between input - desired response (no realism!) Generative Models state space models using neuroscience elements (minimal realism). White models significant realism (wish list!)

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Optimal Linear Model The Wiener (regression) solution Normalized LMS with weight decay is a simple starting point. Four multiplies, one divide and two adds per weight update Ten tap embedding with 105 neurons For 1-D topology contains 1,050 parameters (3,150) Z -1 delay of 1 sample adder w i (n) parameter i at time n w0w9w0w9

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3-D, 2-D Trajectory Modeling and Robot Control Collaboration with Miguel Nicolelis, Duke University Sponsored by DARPA

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Time-Delay Neural Network (TDNN) The first layer is a bank of linear filters followed by a nonlinearity. The number of delays to span I second y(n)= wf(wx(n)) Trained with backpropagation Topology contains a ten tap embedding and five hidden PEs 5,255 weights (1-D) Principe, UF

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Multiple Switching Local Models Multiple adaptive filters that compete to win the modeling of a signal segment. Structure is trained all together with normalized LMS/weight decay Needs to be adapted for input-output modeling. We selected 10 FIR experts of order 10 (105 input channels) d(n)

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Recurrent Multilayer Perceptron (RMLP) Nonlinear Black Box Spatially recurrent dynamical systems Memory is created by feeding back the states of the hidden PEs. Feedback allows for continuous representations on multiple timescales. If unfolded into a TDNN it can be shown to be a universal mapper in R n Trained with backpropagation through time

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Generative Models for BMIs Use partial information about the physiological system, normally in the form of states. They can be either applied to binned data or to spike trains directly. Here we will only cover the spike train implementations. Difficulty of spike train Analysis: Spike trains are point processes, i.e. all the information is contained in the timing of events, not in the amplitude of the signals!

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Particle Filters for Point Processes Kinematic State Neural Tuning function spike trains Prediction Updating NonGaussian P(state|observation) Linear filter nonlinearity f Poisson model kinemati cs spikes Instantaneous tuning model

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Generative Data Modeling .. Neural Channels Time Observable Processes (probed neurons) Hidden Processes (Brain areas)

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BMI lessons learned BMIs are beyond the Proof of Concept stage, but. Present systems are signal translators and will not be the blue print for clinical applications Current decoding methods use kinematic training signals - not available in the paralyzed I/O models cannot contend with new environments without retraining BMIs should not be simply a passive decoder incorporate cognitive abilities of the user

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BMI lessons learned BMIs are beyond the Proof of Concept stage, but. Present systems are signal translators and will not be the blue print for clinical applications Current decoding methods use kinematic training signals - not available in the paralyzed I/O models cannot contend with new environments without retraining BMIs should not be simply a passive decoder incorporate cognitive abilities of the user

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A Paradigm Shift for BMIs! During training the user actions create a desired response to the DSP algorithm. During testing the DSP algorithm creates an approximation to the desired response. DSP algorithm Desired response Neural Signal Processing

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The control algorithm learns through reinforcement to achieve common goals in the environment. Shared control with user to enhance learning in multiple scenarios and acquire the net benefits of behavioral, computational, and physiological strategies X Control Algorithm Learning Algorithm Neural Signal Processing A Paradigm Shift for BMIs!

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Construction of a New Framework How to capitalize on the perception-action cycle? The brain is embodied and the body is embedded Need to quantify Brain State at different time resolutions Intelligent behavior arises from the actions of an individual seeking to maximize received reward in a complex and changing world. The BMI must engage and dialogue with the user: Exploits better engineering knowledge Utilizes cognitive states Dissects behavior top-down Exploits rewards Learns with use Propose Reinforcement Learning to train the BMI. FUTURE PAST INTERNAL REPRESENTATION EXTERNAL WORLD LIMBIC SYSTEM ORGANIZED PAST EXPERIENCE PREDICTIVE MODELING DOES ACTION MEET FUTURE REALITY? SENSORY STIMULUS Causality line Body line

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Reward Learning Involves a Dialogue Relation between the agent and its environment. Environment: You are in state 14. You have 2 possible actions. Agent:I'll take action 2. Environment: You received a reinforcement of 17.8 units. You are now in state 13. You have 2 possible actions. Agent:I'll take action 1. repeat AGENT ENVIRONMENT actions rewards states Goal Start

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CABMI involves TWO intelligent agents in a cooperative dialogue!!! states ROBOT actions rewards RATS BRAIN environment RATS BRAIN COMPUTER AGENT Users neuromodulation sets the value function for the CA Both the CA and the user have the same reward in 3D space

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Features of co-adaptive BMI Enables intelligent system design in BMIs Both systems adapt in close loop in a very tight coupling between brain activity and computer agent ( CA states are specified by brain activity). User must incorporate the CA in its world (can a rat learn this?) CA must decode brain activity for its value function (can it model the signature of behavior?). In fact CABMI is a symbiotic biological- computer hybrid system. 31

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Experiment workspace [top view] The user learns first to associate levers with water reward in a training phase. In brain control, it progressively associates the blue guide LED of the robotic arm with the target lever LEDs. Only when the robot presses the target lever it will get reward.

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Experiment workspace [top view]

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Experimental CABMI Paradigm Incorrect Target Correct Target Starting Position Match LEDs Grid-space Match LEDs Rats Perspective Water Reward Map workspace to grid Rat Robot Arm Left LeverRight Lever 27 discrete actions 26 movements 1 stationary

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Experimental CABMI Paradigm CA rewards are defined in 3D at the target lever positions. RL is used to train the CA in brain control (tabula rasa, i.e. no a priori information). During brain control, shaping of the reward field increases the level of difficulty across multiple sections with an adjustable threshold target. 35

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Neuromodulation defines the States Sampling rate 24.4 kHz Hall, Brain Research (1974) 32 channels Spike sorted data Bilateral Premotor/motor Areas

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Performance metrics Performance metrics: 1.Percentage of trials earning reward 2.Average control time required to reach a target 4 sessions were simulated using random action selection to estimate chance performance for the CABMI in increasing difficulty tasks.

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% trials earning reward time to achieve reward Performance in 4 tasks of increasing difficulty

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Closed-Loop RLBMI Non-functional levers Functional levers Robot workspace in rat visual field of view. BLUE Robot GREEN - Lever Top-view of the rat behavioral cage.

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It is well established that preparation, execution, and also imagination of movement produce an event-related desynchronization (ERD) over the sensorimotor areas, with maxima in the alpha band (mu rhythm, 10 Hz) and beta band (20 Hz). The mu ERD is most prominent over the contralateral sensorimotor areas during motor preparation and extends bilaterally with movement initiation ERD during hand motor imagery is very similar to the pre- movement ERD, i.e., it is locally restricted to the contralateral sensorimotor areas Event Related Desynchronization (ERD) and synchronization (ERS)

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During movement preparation and execution, an increase of synchronization (ERS) in the 10-Hz band normally appears over areas not engaged in the task (idling) ERS can also be observed after the movement, over the same areas that had displayed ERD earlier Event Related Desynchronization (ERD) and synchronization (ERS)

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Beta rebound following movement and somatosensory stimulation The general finding is that beta oscillations are desynchronized during preparation, execution, and imagination of a motor act After movement offset, the beta band activity recovers very fast (