An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic.

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.

Is a simplified Finite Element model of the gluteus region able to capture the mechanical response of the internal soft tissues under compression?

BACKGROUND: Internal soft tissue strains have been shown to be one of the main factors responsible for the onset of Pressure Ulcers and to be representative of its risk of development. However, the estimation of this parameter using Finite Element (FE) analysis in clinical setups is currently hindered by costly acquisition, reconstruction and computation times. Ultrasound (US) imaging is a promising candidate for the clinical assessment of both morphological and material parameters. METHOD: The aim of this study was to investigate the ability of a local FE model of the region beneath the ischium with a limited number of parameters to capture the internal response of the gluteus region predicted by a complete 3D FE model. 26 local FE models were developed, and their predictions were compared to those of the patient-specific reference FE models in sitting position. FINDINGS: A high correlation was observed (R = 0.90, p-value < 0.01). A sensitivity analysis showed that the most influent parameters were the mechanical behaviour of the muscle tissues, the ischium morphology and the external mechanical loading. INTERPRETATION: Given the progress of US for capturing both morphological and material parameters, these results are promising because they open up the possibility to use personalised simplified FE models for risk estimation in daily clinical routine.

An exoskeleton controlled by an epidural wireless brain-machine interface in a tetraplegic patient: a proof-of-concept demonstration.

BACKGROUND: Approximately 20% of traumatic cervical spinal cord injuries result in tetraplegia. Neuroprosthetics are being developed to manage this condition and thus improve the lives of patients. We aimed to test the feasibility of a semi-invasive technique that uses brain signals to drive an exoskeleton. METHODS: We recruited two participants at Clinatec research centre, associated with Grenoble University Hospital, Grenoble, France, into our ongoing clinical trial. Inclusion criteria were age 18-45 years, stability of neurological deficits, a need for additional mobility expressed by the patient, ambulatory or hospitalised monitoring, registration in the French social security system, and signed informed consent. The exclusion criteria were previous brain surgery, anticoagulant treatments, neuropsychological sequelae, depression, substance dependence or misuse, and contraindications to magnetoencephalography (MEG), EEG, or MRI. One participant was excluded because of a technical problem with the implants. The remaining participant was a 28-year-old man, who had tetraplegia following a C4-C5 spinal cord injury. Two bilateral wireless epidural recorders, each with 64 electrodes, were implanted over the upper limb sensorimotor areas of the brain. Epidural electrocorticographic (ECoG) signals were processed online by an adaptive decoding algorithm to send commands to effectors (virtual avatar or exoskeleton). Throughout the 24 months of the study, the patient did various mental tasks to progressively increase the number of degrees of freedom. FINDINGS: Between June 12, 2017, and July 21, 2019, the patient cortically controlled a programme that simulated walking and made bimanual, multi-joint, upper-limb movements with eight degrees of freedom during various reach-and-touch tasks and wrist rotations, using a virtual avatar at home (64·0% [SD 5·1] success) or an exoskeleton in the laboratory (70·9% [11·6] success). Compared with microelectrodes, epidural ECoG is semi-invasive and has similar efficiency. The decoding models were reusable for up to approximately 7 weeks without recalibration. INTERPRETATION: These results showed long-term (24-month) activation of a four-limb neuroprosthetic exoskeleton by a complete brain-machine interface system using continuous, online epidural ECoG to decode brain activity in a tetraplegic patient. Up to eight degrees of freedom could be simultaneously controlled using a unique model, which was reusable without recalibration for up to about 7 weeks. FUNDING: French Atomic Energy Commission, French Ministry of Health, Edmond J Safra Philanthropic Foundation, Fondation Motrice, Fondation Nanosciences, Institut Carnot, Fonds de Dotation Clinatec.

Development and evaluation of a new methodology for the fast generation of patient-specific Finite Element models of the buttock for sitting-acquired deep tissue injury prevention.

The occurrence and management of Pressure Ulcers remain a major issue for patients with reduced mobility and neurosensory loss despite significant improvement in the prevention methods. These injuries are caused by biological cascades leading from a given mechanical loading state in tissues to irreversible tissue damage. Estimating the internal mechanical conditions within loaded soft tissues has the potential of improving the management and prevention of PU. Several Finite Element models of the buttock have therefore been proposed based on either MRI or CT-Scan data. However, because of the limited availability of MRI or CT-Scan systems and of the long segmentation time, all studies in the literature include the data of only one individual. Yet the inter-individual variability can't be overlooked when dealing with patient specific estimation of internal tissue loading. As an alternative, this contribution focuses on the combined use of low-dose biplanar X-ray images, B-mode ultrasound images and optical scanner acquisitions in a non-weight-bearing sitting posture for the fast generation of patient-specific FE models of the buttock. Model calibration was performed based on Ischial Tuberosity sagging. Model evaluation was performed by comparing the simulated contact pressure with experimental observations on a population of 6 healthy subjects. Analysis of the models confirmed the high inter-individual variability of soft tissue response (maximum Green Lagrange shear strains of 213 ±â€¯101% in the muscle). This methodology opens the way for investigating inter-individual factors influencing the soft tissue response during sitting and for providing tools to assess PU risk.

CLINATEC® BCI platform based on the ECoG-recording implant WIMAGINE® and the innovative signal-processing: preclinical results.

The goal of the CLINATEC® Brain Computer Interface (BCI) Project is to improve tetraplegic subjects' quality of life by allowing them to interact with their environment through the control of effectors, such as an exoskeleton. The BCI platform is based on a wireless 64-channel ElectroCorticoGram (ECoG) recording implant WIMAGINE®, designed for long-term clinical application, and a BCI software environment associated to a 4-limb exoskeleton EMY (Enhancing MobilitY). Innovative ECoG signal decoding algorithms will allow the control of the exoskeleton by the subject's brain activity. Currently, the whole BCI platform was tested in real-time in preclinical experiments carried out in nonhuman primates. In these experiments, the exoskeleton arm was controlled by means of the decoded neuronal activity.