Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning
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Abstract
Objective . Brain–machine interfaces (BMIs) seek to restore lost motor functions in individuals with neurological disorders by enabling them to control external devices directly with their thoughts. This work aims to improve robustness and decoding accuracy that currently become major challenges in the clinical translation of intracortical BMIs. Approach . We propose entire spiking activity (ESA)—an envelope of spiking activity that can be extracted by a simple, threshold-less, and automated technique—as the input signal. We couple ESA with deep learning-based decoding algorithm that uses quasi-recurrent neural network (QRNN) architecture. We evaluate comprehensively the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded from the primary motor cortex area of three non-human primates performing different tasks. Main results . Our proposed method yields consistently higher decoding performance than any other combinations of the input signal and decoding algorithm previously reported across long-term recording sessions. It can sustain high decoding performance even when removing spikes from the raw signals, when using the different number of channels, and when using a smaller amount of training data. Significance . Overall results demonstrate exceptionally high decoding accuracy and chronic robustness, which is highly desirable given it is an unresolved challenge in BMIs.
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Multi-unit activity approaches have been shown to provide robust performance on decoding tasks.
“Such multi-unit activity approaches are computationally efficient to estimate online and have been show to provide an accurate representation of neural activity [59] as well as robust performance on decoding tasks [60].”
From Deep Learning–Based Control of Electrically Evoked Activity in Human Visual Cortex (2025) · cites this paper as [60]
Section 3.3 (page unavailable) reports that multi-unit activity (MUA) achieved the second-highest average hand-kinematics decoding performance among the signals compared. But the paper attributes robust decoding under varying conditions to entire spiking activity (ESA), not MUA, so it does not establish the claimed robustness of MUA approaches. (LLM, conf=0.83)
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“Such multi-unit activity approaches are computationally efficient to estimate online and have been show to provide an accurate representation of neural activity [59] as well as robust performance on decoding tasks [60].”
- Authors
- Ahmadi, N., Constandinou, T. G., & Bouganis, C.-S.
- Affiliations
- Imperial College London (GB)
- UK Dementia Research Institute (GB)
- Corresponding author
- Ahmadi, Nur
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2021-02-26
- Volume
- 18
- Issue
- 2
- Pages
- 026011
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W3126124476
- Cited by
- 69
- References
- 101
- Citations by year
- 2026: 9 · 2025: 14 · 2024: 18 · 2023: 13 · 2022: 7 · 2021: 7 · 2020: 1
- FWCI
- 5.92
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Funders
- Indonesia Endowment Fund for Education (PRJ-123/LPDP/2016)
- Engineering and Physical Sciences Research Council (EP/M020975/1)
- 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, intracortical recording, primary motor cortex, non-human primates, neural decoding, motor restoration
- MeSH terms
- Deep Learning, Animals, Biomechanical Phenomena, Motor Cortex, Neural Networks, Computer, Brain-Computer Interfaces
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