BCI decoder performance comparison of an LSTM recurrent neural network and a Kalman filter in retrospective simulation Article Swipe
Tommy Hosman
,
Marco Vilela
,
Daniel Milstein
,
Jessica N. Kelemen
,
David M. Brandman
,
Leigh R. Hochberg
,
John D. Simeral
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/ner.2019.8717140
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/ner.2019.8717140
Intracortical brain computer interfaces (iBCIs) using linear Kalman decoders have enabled individuals with paralysis to control a computer cursor for continuous point-and-click typing on a virtual keyboard, browsing the internet, and using familiar tablet apps. However, further advances are needed to deliver iBCI-enabled cursor control approaching able-bodied performance. Motivated by recent evidence that nonlinear recurrent neural networks (RNNs) can provide higher performance iBCI cursor control in nonhuman primates (NHPs), we evaluated decoding of intended cursor velocity from human motor cortical signals using a long-short term memory (LSTM) RNN trained across multiple days of multi-electrode recordings. Running simulations with previously recorded intracortical signals from three BrainGate iBCI trial participants, we demonstrate an RNN that can substantially increase bits-per-second metric in a high-speed cursor-based target selection task as well as a challenging small-target high-accuracy task when compared to a Kalman decoder. These results indicate that RNN decoding applied to human intracortical signals could achieve substantial performance advances in continuous 2-D cursor control and motivate a real-time RNN implementation for online evaluation by individuals with tetraplegia.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/ner.2019.8717140
- OA Status
- green
- Cited By
- 6
- References
- 28
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2906513432
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2906513432Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/ner.2019.8717140Digital Object Identifier
- Title
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BCI decoder performance comparison of an LSTM recurrent neural network and a Kalman filter in retrospective simulationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-03-01Full publication date if available
- Authors
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Tommy Hosman, Marco Vilela, Daniel Milstein, Jessica N. Kelemen, David M. Brandman, Leigh R. Hochberg, John D. SimeralList of authors in order
- Landing page
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https://doi.org/10.1109/ner.2019.8717140Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/1812.09835Direct OA link when available
- Concepts
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Computer science, Cursor (databases), Brain–computer interface, Recurrent neural network, Kalman filter, Decoding methods, Artificial intelligence, Speech recognition, Computer vision, Artificial neural network, Electroencephalography, Neuroscience, Psychology, AlgorithmTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
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2024: 1, 2021: 1, 2020: 2, 2019: 2Per-year citation counts (last 5 years)
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28Number of works referenced by this work
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20Other works algorithmically related by OpenAlex
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