Reducing power requirements for high-accuracy decoding in iBCIs Article Swipe
Brianna M. Karpowicz
,
Bareesh Bhaduri
,
Samuel R. Nason
,
Brandon G. Jacques
,
Yahia H Ali
,
Robert D. Flint
,
Payton H Bechefsky
,
Leigh R. Hochberg
,
Nicholas Au Yong
,
Marc W. Slutzky
,
Chethan Pandarinath
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1741-2552/ad88a4
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1741-2552/ad88a4
Objective. Current intracortical brain-computer interfaces (iBCIs) rely predominantly on threshold crossings (‘spikes’) for decoding neural activity into a control signal for an external device. Spiking data can yield high accuracy online control during complex behaviors; however, its dependence on high-sampling-rate data collection can pose challenges. An alternative signal for iBCI decoding is the local field potential (LFP), a continuous-valued signal that can be acquired simultaneously with spiking activity. However, LFPs are seldom used alone for online iBCI control as their decoding performance has yet to achieve parity with spikes. Approach. Here, we present a strategy to improve the performance of LFP-based decoders by first training a neural dynamics model to use LFPs to reconstruct the firing rates underlying spiking data, and then decoding from the estimated rates. We test these models on previously-collected macaque data during center-out and random-target reaching tasks as well as data collected from a human iBCI participant during attempted speech. Main results. In all cases, training models from LFPs enables firing rate reconstruction with accuracy comparable to spiking-based dynamics models. In addition, LFP-based dynamics models enable decoding performance exceeding that of LFPs alone and approaching that of spiking-based models. In all applications except speech, LFP-based dynamics models also facilitate decoding accuracy exceeding that of direct decoding from spikes. Significance. Because LFP-based dynamics models operate on lower bandwidth and with lower sampling rate than spiking models, our findings indicate that iBCI devices can be designed to operate with lower power requirements than devices dependent on recorded spiking activity, without sacrificing high-accuracy decoding.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1741-2552/ad88a4
- OA Status
- hybrid
- Cited By
- 1
- References
- 78
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403528025
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4403528025Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1741-2552/ad88a4Digital Object Identifier
- Title
-
Reducing power requirements for high-accuracy decoding in iBCIsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-18Full publication date if available
- Authors
-
Brianna M. Karpowicz, Bareesh Bhaduri, Samuel R. Nason, Brandon G. Jacques, Yahia H Ali, Robert D. Flint, Payton H Bechefsky, Leigh R. Hochberg, Nicholas Au Yong, Marc W. Slutzky, Chethan PandarinathList of authors in order
- Landing page
-
https://doi.org/10.1088/1741-2552/ad88a4Publisher landing page
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1088/1741-2552/ad88a4Direct OA link when available
- Concepts
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Decoding methods, Local field potential, Computer science, Neural decoding, Artificial intelligence, Spiking neural network, SIGNAL (programming language), Artificial neural network, Algorithm, Neuroscience, Programming language, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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78Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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