Design and analysis of closed-loop decoder adaptation algorithms for brain-machine interfaces
Abstract
Closed-loop decoder adaptation (CLDA) is an emerging paradigm for achieving rapid performance improvements in online brain-machine interface (BMI) operation. Designing an effective CLDA algorithm requires making multiple important decisions, including choosing the timescale of adaptation, selecting which decoder parameters to adapt, crafting the corresponding update rules, and designing CLDA parameters. These design choices, combined with the specific settings of CLDA parameters, will directly affect the algorithm's ability to make decoder parameters converge to values that optimize performance. In this article, we present a general framework for the design and analysis of CLDA algorithms and support our results with experimental data of two monkeys performing a BMI task. First, we analyze and compare existing CLDA algorithms to highlight the importance of four critical design elements: the adaptation timescale, selective parameter adaptation, smooth decoder updates, and intuitive CLDA parameters. Second, we introduce mathematical convergence analysis using measures such as mean-squared error and KL divergence as a useful paradigm for evaluating the convergence properties of a prototype CLDA algorithm before experimental testing. By applying these measures to an existing CLDA algorithm, we demonstrate that our convergence analysis is an effective analytical tool that can ultimately inform and improve the design of CLDA algorithms.
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The plasticity/stability dilemma with respect to MI BCI co-adaptation has not been adequately studied so far.
“Since the plasticity/stability dilemma with respect to MI BCI co-adaptation has not been adequately studied so far [21,52], we believe that a parsimonious approach eventually trading off decoding accuracy in the short term in order to better fulfill the subject learning objective in the long term, as done here, is preferable.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [52]
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- Authors
- Dangi, S., Orsborn, A. L., Moorman, H. G., & Carmena, J. M.
- Affiliations
- University of California, Berkeley (US)
- University of California, San Francisco (US)
- Venue
- Neural Computation
- Publisher
- MIT Press
- Published
- 2013-07
- Volume
- 25
- Issue
- 7
- Pages
- 1693-1731
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2139845258
- Cited by
- 103
- References
- 54
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 7 · 2023: 11 · 2022: 7 · 2021: 7 · 2020: 6 · 2019: 5 · 2018: 10 · 2017: 6 · 2016: 6 · 2015: 6 · 2014: 15 · 2013: 7
- FWCI
- 6.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neural Computation (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- brain-computer interface, online decoding, convergence analysis, mean squared error, Kullback-Leibler divergence, primate motor control
- MeSH terms
- Action Potentials, Adaptation, Physiological, Algorithms, Animals, Arm, Macaca mulatta, Models, Neurological, Motor Cortex, Motor Neurons, Movement, Neural Pathways, Time Factors, Visual Cortex, Likelihood Functions, Feedback, Physiological, Brain-Computer Interfaces
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