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Adaptive Decoding for Brain-Machine Interfaces Through Bayesian Parameter Updates

Overview of attention for article published in Neural Computation, December 2011
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Title
Adaptive Decoding for Brain-Machine Interfaces Through Bayesian Parameter Updates
Published in
Neural Computation, December 2011
DOI 10.1162/neco_a_00207
Pubmed ID
Authors

Zheng Li, Joseph E. O'Doherty, Mikhail A. Lebedev, Miguel A. L. Nicolelis

Abstract

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.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 5%
Japan 2 2%
Germany 2 2%
Mexico 1 <1%
Unknown 99 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 39 36%
Researcher 17 16%
Student > Master 10 9%
Professor 6 6%
Student > Postgraduate 5 5%
Other 20 18%
Unknown 12 11%
Readers by discipline Count As %
Engineering 40 37%
Agricultural and Biological Sciences 22 20%
Neuroscience 12 11%
Medicine and Dentistry 8 7%
Computer Science 7 6%
Other 4 4%
Unknown 16 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 May 2019.
All research outputs
#8,665,632
of 14,854,534 outputs
Outputs from Neural Computation
#531
of 862 outputs
Outputs of similar age
#59,319
of 101,744 outputs
Outputs of similar age from Neural Computation
#3
of 4 outputs
Altmetric has tracked 14,854,534 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 862 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
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