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Brain-Machine Interface (BMI) System Identification

Brain-Machine Interface (BMI) System Identification. Siddharth Dangi and Suraj Gowda 12/13/10. Brain-Machine Interfaces (BMIs). Decode neural activity into control signals for assistive devices such as computer cursors and prosthetic limbs.

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Brain-Machine Interface (BMI) System Identification

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  1. Brain-Machine Interface (BMI) System Identification Siddharth Dangi and Suraj Gowda 12/13/10

  2. Brain-Machine Interfaces (BMIs) • Decode neural activity into control signals for assistive devices such as computer cursors and prosthetic limbs • Aim to improve quality of life for severely disabled patients suffering from neurological injuries and disease • Restore a human’s ability to move and communicate with the world

  3. “Center-Out” Training Task • Monkey uses joystick to move a cursor to targets • Record neural firing rates and cursor kinematic data • Train decoding algorithm using collected data to predict cursor kinematics • Switch cursor control from joystick to decoder

  4. Echo-State Network (ESN) • Training connections inside the reservoir is difficult and computationally expensive • Use supervised learning to train only the output layer weights • Problem – relationship between neural signals and limb kinematics is highly nonlinear • Idea – create a large, recurrent neural network with random weights • Can be used to learn the input-output behavior of a nonlinear system

  5. Kalman Filter-based methods • Adaptive Kalman filter • Allow parameters to auto-adjust • Stochastic gradient descent • Standard model • State prediction Kinematic state at time t Firing rates at time t Gaussian noise variables • Combined Kalman-ESN method • Weight estimates based on error variances

  6. LMS and Wiener Filter Wiener Filter • Model: • Rewritten by tiling collected data as: • Solution: Least-Mean Squares (LMS) • Model: • Update equation for weight matrix: Kinematic state at time n Filter weights Firing rates at time n Offset term Error term at time n Filter lag

  7. Performance Results

  8. Prediction Results Two metrics – Mean-Squared Error (MSE) and Correlation Coefficient (CC)

  9. Classification of Neural State • Neuron firing rates signals can be treated as (behavior-driven) state-space trajectories • Experiment – use logistic regression to classify trajectories into higher-level states (e.g., planning vs. not planning) • Classes: • Logistic Regression Model: • Online Estimation Algorithm • 93.1% classification accuracy • Most errors were “false alarm” before/after training periods

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