1. An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic
- Author
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Alexandre Moly, Thomas Costecalde, Félix Martel, Matthieu Martin, Christelle Larzabal, Serpil Karakas, Alexandre Verney, Guillaume Charvet, Stephan Chabardes, Alim Louis Benabid, Tetiana Aksenova, Clinatec - Centre de recherche biomédicale Edmond J.Safra (SCLIN), Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre Hospitalier Universitaire [Grenoble] (CHU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Hôpital la Tronche, CHU Grenoble, and Université Joseph Fourier - Grenoble 1 (UJF)-CHU Grenoble
- Subjects
Epidural Space ,Signal Processing (eess.SP) ,FOS: Computer and information sciences ,Computer Science - Machine Learning ,exoskeleton control ,Clinical Studies as Topic ,brain-computer interface ,adaptive ,Biomedical Engineering ,Exoskeleton Device ,ECoG ,Machine Learning (cs.LG) ,mixture of experts ,Computer Science - Robotics ,Cellular and Molecular Neuroscience ,tetraplegic ,Brain-Computer Interfaces ,Linear Models ,FOS: Electrical engineering, electronic engineering, information engineering ,Humans ,Electrocorticography ,Electrical Engineering and Systems Science - Signal Processing ,BCI ,[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] ,Robotics (cs.RO) - Abstract
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.
- Published
- 2022
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