Ascertaining the Importance of Neurons to Develop Better Brain-Machine Interfaces
Abstract
In the design of brain-machine interface (BMI) algorithms, the activity of hundreds of chronically recorded neurons is used to reconstruct a variety of kinematic variables. A significant problem introduced with the use of neural ensemble inputs for model building is the explosion in the number of free parameters. Large models not only affect model generalization but also put a computational burden on computing an optimal solution especially when the goal is to implement the BMI in low-power, portable hardware. In this paper, three methods are presented to quantitatively rate the importance of neurons in neural to motor mapping, using single neuron correlation analysis, sensitivity analysis through a vector linear model, and a model-independent cellular directional tuning analysis for comparisons purpose. Although, the rankings are not identical, up to sixty percent of the top 10 ranking cells were in common. This set can then be used to determine a reduced-order model whose performance is similar to that of the ensemble. It is further shown that by pruning the initial ensemble neural input with the ranked importance of cells, a reduced sets of cells (between 40 and 80, depending upon the methods) can be found that exceed the BMI performance levels of the full ensemble.
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Multiplexing a high-density electrode array to a smaller amplifier array is a widely used resource-sharing technique that relies on not all electrode sites providing useful information after implantation or on necessary information being obtainable from a small subset, although the useful subset is not known in advance.
“The first widely used technique consists of multiplexing a high-density electrode array to a much smaller amplifier array. This strategy leverages the fact that not all electrode sites may provide useful information after implantation [97, 101, 145] or that the necessary information can be obtained from a small subset of electrodes, although the useful electrode subset is not known a priori [118].”
From Implantable neurotechnologies: a review of integrated circuit neural amplifiers (2016) · cites this paper as [118]
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- Authors
- Sanchez, J. C., Carmena, J. M., Lebedev, M. A., Nicolelis, M. A. L., Harris, J. G., & Principe, J. C.
- Affiliations
- University of Florida (US)
- Duke University (US)
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2004-06
- Volume
- 51
- Issue
- 6
- Pages
- 943-953
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2103554020
- Cited by
- 111
- References
- 31
- Citations by year
- 2025: 1 · 2020: 2 · 2019: 6 · 2018: 3 · 2017: 2 · 2016: 5 · 2015: 7 · 2014: 9 · 2013: 6 · 2012: 6
- FWCI
- 6.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Universidade de São Paulo
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- brain-computer interface, model reduction, sensitivity analysis, model generalization, low-power electronics
- MeSH terms
- Action Potentials, Algorithms, Animals, Cerebral Cortex, Computer Simulation, Electroencephalography, Female, Hand, Macaca, Models, Neurological, Movement, Nerve Net, Neurons, Sensitivity and Specificity, Signal Processing, Computer-Assisted, User-Computer Interface, Reproducibility of Results, Models, Statistical, Likelihood Functions
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