An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Brain–machine interfaces (BMIs) promise to restore movement and communication in people with paralysis and ultimately allow the human brain to interact seamlessly with external devices, paving the way for a new wave of medical and consumer technology. However, neural activity can adapt and change over time, presenting a substantial challenge for reliable BMI implementation. Large-scale recordings in animal studies now allow us to study how behavioral information is distributed in multiple brain areas, and state-of-the-art interfaces now incorporate models of the brain as a feedback controller. Ongoing research aims to understand the impact of neural plasticity on BMIs and find ways to leverage learning while accommodating unexpected changes in the neural code. We review the current state of experimental and clinical BMI research, focusing on what we know about the neural code, methods for optimizing decoders for closed-loop control, and emerging strategies for addressing neural plasticity. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Designing Closed-Loop Brain-Machine Interfaces Using Model Predictive Control

Technologies ◽

10.3390/technologies4020018 ◽

2016 ◽

Vol 4 (2) ◽

pp. 18

Author(s):

Gautam Kumar ◽

Mayuresh Kothare ◽

Nitish Thakor ◽

Marc Schieber ◽

Hongguang Pan ◽

...

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Closed Loop ◽

Brain Machine Interfaces

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Dynamic Analysis of Naive Adaptive Brain-Machine Interfaces

Neural Computation ◽

10.1162/neco_a_00484 ◽

2013 ◽

Vol 25 (9) ◽

pp. 2373-2420 ◽

Cited By ~ 11

Author(s):

Kevin C. Kowalski ◽

Bryan D. He ◽

Lakshminarayan Srinivasan

Keyword(s):

Closed Loop ◽

Human Subjects ◽

Critical Role ◽

Neuromuscular Diseases ◽

Arm Movements ◽

Training Data ◽

Neural Signals ◽

Brain Machine Interfaces ◽

User Intent ◽

Novel Approach

The closed-loop operation of brain-machine interfaces (BMI) provides a context to discover foundational principles behind human-computer interaction, with emerging clinical applications to stroke, neuromuscular diseases, and trauma. In the canonical BMI, a user controls a prosthetic limb through neural signals that are recorded by electrodes and processed by a decoder into limb movements. In laboratory demonstrations with able-bodied test subjects, parameters of the decoder are commonly tuned using training data that include neural signals and corresponding overt arm movements. In the application of BMI to paralysis or amputation, arm movements are not feasible, and imagined movements create weaker, partially unrelated patterns of neural activity. BMI training must begin naive, without access to these prototypical methods for parameter initialization used in most laboratory BMI demonstrations. Naive adaptive BMI refer to a class of methods recently introduced to address this problem. We first identify the basic elements of existing approaches based on adaptive filtering and define a decoder, ReFIT-PPF to represent these existing approaches. We then present Joint RSE, a novel approach that logically extends prior approaches. Using recently developed human- and synthetic-subjects closed-loop BMI simulation platforms, we show that Joint RSE significantly outperforms ReFIT-PPF and nonadaptive (static) decoders. Control experiments demonstrate the critical role of jointly estimating neural parameters and user intent. In addition, we show that nonzero sensorimotor delay in the user significantly degrades ReFIT-PPF but not Joint RSE, owing to differences in the prior on intended velocity. Paradoxically, substantial differences in the nature of sensory feedback between these methods do not contribute to differences in performance between Joint RSE and ReFIT-PPF. Instead, BMI performance improvement is driven by machine learning, which outpaces rates of human learning in the human-subjects simulation platform. In this regime, nuances of error-related feedback to the human user are less relevant to rapid BMI mastery.

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Brain-Machine Interfaces: The Perception-Action Closed Loop: A Two-Learner System

IEEE Systems Man and Cybernetics Magazine ◽

10.1109/msmc.2014.2386901 ◽

2015 ◽

Vol 1 (1) ◽

pp. 6-8 ◽

Cited By ~ 10

Author(s):

Jose del R. Millan

Keyword(s):

Closed Loop ◽

Brain Machine Interfaces ◽

Perception Action

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Designing closed-loop brain-machine interfaces with network of spiking neurons using MPC strategy

2015 American Control Conference (ACC) ◽

10.1109/acc.2015.7171117 ◽

2015 ◽

Author(s):

Hongguang Pan ◽

Baocang Ding ◽

Weimin Zhong ◽

Gautam Kumar ◽

Mayuresh V. Kothare

Keyword(s):

Closed Loop ◽

Spiking Neurons ◽

Brain Machine Interfaces

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Adaptive Decoding for Brain-Machine Interfaces Through Bayesian Parameter Updates

Neural Computation ◽

10.1162/neco_a_00207 ◽

2011 ◽

Vol 23 (12) ◽

pp. 3162-3204 ◽

Cited By ~ 80

Author(s):

Zheng Li ◽

Joseph E. O'Doherty ◽

Mikhail A. Lebedev ◽

Miguel A. L. Nicolelis

Keyword(s):

Linear Regression ◽

Closed Loop ◽

Unscented Kalman Filter ◽

Bayesian Regression ◽

Brain Machine Interfaces ◽

Neuronal Populations ◽

Long Time ◽

Bayesian Linear Regression ◽

Adaptive Decoding ◽

Control Accuracy

Brain-machine interfaces (BMIs) transform the activity of neurons recorded in motor areas of the brain into movements of external actuators. Representation of movements by neuronal populations varies over time, during both voluntary limb movements and movements controlled through BMIs, due to motor learning, neuronal plasticity, and instability in recordings. To ensure accurate BMI performance over long time spans, BMI decoders must adapt to these changes. We propose the Bayesian regression self-training method for updating the parameters of an unscented Kalman filter decoder. This novel paradigm uses the decoder's output to periodically update its neuronal tuning model in a Bayesian linear regression. We use two previously known statistical formulations of Bayesian linear regression: a joint formulation, which allows fast and exact inference, and a factorized formulation, which allows the addition and temporary omission of neurons from updates but requires approximate variational inference. To evaluate these methods, we performed offline reconstructions and closed-loop experiments with rhesus monkeys implanted cortically with microwire electrodes. Offline reconstructions used data recorded in areas M1, S1, PMd, SMA, and PP of three monkeys while they controlled a cursor using a handheld joystick. The Bayesian regression self-training updates significantly improved the accuracy of offline reconstructions compared to the same decoder without updates. We performed 11 sessions of real-time, closed-loop experiments with a monkey implanted in areas M1 and S1. These sessions spanned 29 days. The monkey controlled the cursor using the decoder with and without updates. The updates maintained control accuracy and did not require information about monkey hand movements, assumptions about desired movements, or knowledge of the intended movement goals as training signals. These results indicate that Bayesian regression self-training can maintain BMI control accuracy over long periods, making clinical neuroprosthetics more viable.

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