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Innovative decoding algorithms for Chronic ECoG-based Brain Computer Interface (BCI) for motor disabled subjects in laboratory and at home

Abstract : Brain-computer interfaces (BCIs) are systems that allow the control of external devices from the brain’s neural signals without neuromuscular activation. Among the various applications, functional compensation and rehabilitation of individuals suffering from severe motor disabilities (with motor BCIs) has always been a focus for BCI research. Brain signals are translated, through signal processing steps, into orders realized by an effector which returns feedbacks (visual, tactile, proprioceptive…) to the patient giving him back some mobility and autonomy. Nevertheless, numerous challenges to translate BCI from offline experiments based on healthy subjects recordings to daily life applications for disable patients. Even though BCI decoding highlights good control performance during specific task such as center out experiments, the development of decoder for online asynchronous decoding, stable during long period, is still one of the BCI community claim. Moreover, a lack of studies and algorithms on multi-limb effector control were highlighted.Relying on the “BCI and Tetraplegia” clinical trial of CEA/LETI/CLINATEC, the development of new decoders for real-time closed-loop adaptive asynchronous multi-limb is addressed in the present doctoral thesis. Recursive exponentially weighted Markov switching multi-linear model (REW-MSLM) was designed to handle complex / high dimensional multi-limb effector control with online closed-loop calibration of the decoding model.Based on a mixture of expert architecture, REW-MSLM allows a tetraplegic patient who underwent bilateral epidural electrocorticographic (ECoG) arrays implantation of chronic wireless implants (WIMAGINE) 8D control of a whole body exoskeleton over several months without model recalibration. The patient was able to perform alternative 3D left and right hand translations and 1D left and right wrist rotations with high accuracy and during long period without any model recalibration. Experiments with higher controlled dimensions and other effectors such as wheelchair have also been tested and highlighted promising results. This PhD thesis aims to present new innovative adaptive BCI decoder adapted to multi-limb decoding for clinical applications and highlights the interest of such decoder in the perspective of the current state-of-the-art.
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Submitted on : Friday, July 23, 2021 - 3:23:12 PM
Last modification on : Monday, July 26, 2021 - 3:07:59 PM


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  • HAL Id : tel-03298314, version 1



Alexandre Moly. Innovative decoding algorithms for Chronic ECoG-based Brain Computer Interface (BCI) for motor disabled subjects in laboratory and at home. Human health and pathology. Université Grenoble Alpes [2020-..], 2020. English. ⟨NNT : 2020GRALS028⟩. ⟨tel-03298314⟩



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