Brain Computer Interface: Neuroprosthetic Control of a Motorized Exoskeleton

Overview

The BCI project falls within the very broad field of brain machine interfaces. Its multiple applications include the compensation of motor deficits. The subject of the present protocol is the first test of the system in man on the compensation of motor deficits by an epidural brain implant enabling an electrocorticogram (EcoG) to be recorded.

Detailed description

Injuries to the cervical spine and to its contents, the spinal cord, cause serious neurological deficits, with loss of motor function and sensitivity of the four limbs, resulting in quadriplegia. The level of the lesion separating the area without deficits, above the lesion, from the sub-lesional area depends on the extent of the spine injury (dislocation, fracture or trauma without final displacement), may cause spinal cord injuries of varying severity, which can range from the benign to a complete section that results in complete and irreversible sensorimotor deficits. Lesions from C1 to C4 are often immediately fatal or cause diaphragmatic paralysis (innervated by the phrenic nerve whose roots originate at C4). C4-C5 paraplegia and below are therefore compatible with life as they spare respiratory autonomy, although they lead to severe permanent disabilities, creating a state of severe dependence in subjects who are often young.

The problems created by these patients are those of an extremely heavy individual, family, and societal burden in addition to the individual drama. While paraplegics, by maintaining their motor skills and sensitivity of both upper limbs and back muscles can often reintegrate and find remarkable mobility with wheelchairs, this is not the case of quadriplegics who must be provided with substitutes in order to achieve an acceptable quality of life. This project offers a highly innovative approach by means of a motorized exoskeleton that enables standing, walking and the use of the upper extremities. The validation of the first step of this concept will pave the way for developing increasingly sophisticated exoskeletal neuroprostheses, aimed at giving these patients compatible and ever greater autonomy.

Interventions

• Device Brain-computer interface (BCI) platform including two implanted remotely powered ElectroCorticoGraph (ECoG) recording devices and an exoskeleton

Primary outcome measures

• Number of Participants with Adverse Events [Time frame: 4 years after surgery]

Secondary outcome measures (2)

• Patient's level of performance in piloting the degrees of freedom of the exoskeleton [Time frame: 4 years after surgery]
• Patient's quality of life evaluation [Time frame: 4 years after surgery]

Eligibility criteria

Inclusion criteria

• Male or female aged between 18 and 45 years
• Stability of neurological deficits in accrued sequelae
• Lack of adequate compensation for the deficits in terms of quality of life. In other words, the expression by the patient of a need for additional mobility, oriented towards greater autonomy
• Ambulatory or hospitalized monitoring
• Fluent in French and able to understand the study procedures, including completing the auto-questionnaires
• Registered in the French social security scheme
• Signed informed consent of the patient will be collected before inclusion in the study

Exclusion criteria

• Previous brain surgery,
• Chronic prescription of anticoagulant treatments,
• Impaired neuropsychological sequelae from an associated head injury,
• Depressive syndrome with or without suicide attempt.
• Alcohol or other substance dependence in the last 12 months, with abuse in the - A complete assessment (neurological and neuropsychological) will be conducted among eligible patients.
• Contraindication to Magnetoencephalography (MEG) and/or Electroencephalography (EEG)
• Contraindication to Magnetic resonance imaging (MRI)

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation

N/A

Model

Single group

Masking

Open label

Primary purpose

Treatment

Study locations

France · 1 center

• CLINATEC — Grenoble

Publications

• Benabid AL, Costecalde T, Eliseyev A, Charvet G, Verney A, Karakas S, Foerster M, Lambert A, Moriniere B, Abroug N, Schaeffer MC, Moly A, Sauter-Starace F, Ratel D, Moro C, Torres-Martinez N, Langar L, Oddoux M, Polosan M, Pezzani S, Auboiroux V, Aksenova T, Mestais C, Chabardes S. An exoskeleton controlled by an epidural wireless brain-machine interface in a tetraplegic patient: a proof-of-concep PMID 31587955
• Larzabal C, Bonnet S, Costecalde T, Auboiroux V, Charvet G, Chabardes S, Aksenova T, Sauter-Starace F. Long-term stability of the chronic epidural wireless recorder WIMAGINE in tetraplegic patients. J Neural Eng. 2021 Sep 9;18(5). doi: 10.1088/1741-2552/ac2003. PMID 34425566
• Detection of Error Correlates in the Motor Cortex in a Long Term Clinical Trial of ECoG based Brain Computer Interface DOI: 10.5220/0010227800260034
• Bellicha A, Struber L, Pasteau F, Juillard V, Devigne L, Karakas S, Chabardes S, Babel M, Charvet G. Depth-sensor-based shared control assistance for mobility and object manipulation: toward long-term home-use of BCI-controlled assistive robotic devices. J Neural Eng. 2025 Feb 14;22(1). doi: 10.1088/1741-2552/adae36. PMID 39854845
• Sliwowski M, Martin M, Souloumiac A, Blanchart P, Aksenova T. Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performance. Front Hum Neurosci. 2023 Mar 16;17:1111645. doi: 10.3389/fnhum.2023.1111645. eCollection 2023. PMID 37007675
• Sliwowski M, Martin M, Souloumiac A, Blanchart P, Aksenova T. Decoding ECoG signal into 3D hand translation using deep learning. J Neural Eng. 2022 Mar 31;19(2). doi: 10.1088/1741-2552/ac5d69. PMID 35287119
• Moly A, Costecalde T, Martel F, Martin M, Larzabal C, Karakas S, Verney A, Charvet G, Chabardes S, Benabid AL, Aksenova T. An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic. J Neural Eng. 2022 Mar 30;19(2). doi: 10.1088/1741-2552/ac59a0. PMID 35234665
• Larzabal C, Auboiroux V, Karakas S, Charvet G, Benabid AL, Chabardes S, Costecalde T, Bonnet S. The Riemannian spatial pattern method: mapping and clustering movement imagery using Riemannian geometry. J Neural Eng. 2021 Apr 8;18(5). doi: 10.1088/1741-2552/abf291. PMID 33770779

Identifiers

NCT: NCT02550522 · BCI and Tetraplegia

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗