An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic
Open accessAbstract
Objective. The article aims at addressing 2 challenges to step motor brain-computer interface (BCI) out of laboratories: asynchronous control of complex bimanual effectors with large numbers of degrees of freedom, using chronic and safe recorders, and the decoding performance stability over time without frequent decoder recalibration. Approach. Closed-loop adaptive/incremental decoder training is one strategy to create a model stable over time. Adaptive decoders update their parameters with new incoming data, optimizing the model parameters in real time. It allows cross-session training with multiple recording conditions during closed loop BCI experiments. In the article, an adaptive tensor-based recursive exponentially weighted Markov-switching multi-linear model (REW-MSLM) decoder is proposed. REW-MSLM uses a mixture of expert (ME) architecture, mixing or switching independent decoders (experts) according to the probability estimated by a ‘gating’ model. A Hidden Markov model approach is employed as gating model to improve the decoding robustness and to provide strong idle state support. The ME architecture fits the multi-limb paradigm associating an expert to a particular limb or action. Main results. Asynchronous control of an exoskeleton by a tetraplegic patient using a chronically implanted epidural electrocorticography (EpiCoG) recorder is reported. The stable over a period of six months (without decoder recalibration) eight-dimensional alternative bimanual control of the exoskeleton and its virtual avatar is demonstrated. Significance. Based on the long-term (>36 months) chronic bilateral EpiCoG recordings in a tetraplegic (ClinicalTrials.gov, NCT02550522), we addressed the poorly explored field of asynchronous bimanual BCI. The new decoder was designed to meet to several challenges: the high-dimensional control of a complex effector in experiments closer to real-world behavior (point-to-point pursuit versus conventional center-out tasks), with the ability of the BCI system to act as a stand-alone device switching between idle and control states, and a stable performance over a long period of time without decoder recalibration.
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A comparable robustness and stability of this computational and technological brain decoding framework has now been observed in two additional individuals with tetraplegia.
“Third, a comparable robustness and stability of this computational and technological brain decoding framework has now been observed in two additional individuals with tetraplegia3,26,29.”
From Walking naturally after spinal cord injury using a brain–spine interface (2023) · cites this paper as [26]
The Clinical trial description (§2.4; page not provided) says the decoder was tested on a single patient. Limitations and perspectives (§4.4; page not provided) likewise says the stability results were demonstrated for a single patient and that implantation of more patients was planned, contrary to the claim that comparable results had already been observed in two additional individuals. (LLM, conf=0.88)
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Walking naturally after spinal cord injury using a brain–spine interface
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“In parallel to the current model use for predictions, the REW-MSLM decoders update their parameters on the basis of new incoming data, which enables the optimization of the parameters of the model in real time throughout the calibration session 26 .”
BackgroundMethodInfluentialSelf-citationLuo, S., Angrick, M., Coogan, C., Candrea, D. N., Wyse‐Sookoo, K., Shah, S., Rabbani, Q., Milsap, G. W., Weiss, A. R., Anderson, W. S., et al. (2023) · Advanced Science · doi:10.1002/advs.202304853 · Cited by 90 · see citing works
- Authors
- Moly, A., Costecalde, T., Martel, F., Martin, M., Larzabal, C., Karakas, S., Verney, A., Charvet, G., Chabardes, S., Benabid, A. L., & Aksenova, T.
- Affiliations
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Corresponding author
- Martel, Félix
- Aksenova, Tetiana
- ORCID
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2022-03-30
- Volume
- 19
- Issue
- 2
- Pages
- 026021
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W4214878375
- Cited by
- 65
- References
- 72
- Citations by year
- 2026: 10 · 2025: 29 · 2024: 11 · 2023: 11 · 2022: 4
- FWCI
- 7.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- Ministère de la Santé (PHRC-15-15-0124)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Keywords
- brain-computer interface, exoskeleton control, tetraplegia, chronic neural recording, mixture of experts, asynchronous control, avatars
- MeSH terms
- Exoskeleton Device, Clinical Studies as Topic, Electrocorticography, Epidural Space, Humans, Linear Models, Brain-Computer Interfaces
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