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Thesis
Home   /   Post Doctorat   /   Auto-adaptive neural decoder for clinical brain-spine interfacing

Auto-adaptive neural decoder for clinical brain-spine interfacing

Engineering sciences Health and environment technologies, medical devices Mathematics - Numerical analysis - Simulation Technological challenges

Abstract

CEA/LETI/CLINATEC invite applications for postdoctoral position to work on the HORIZON-EIC project. The project goal is to explore novel solutions for functional rehabilitation and/or compensation for people with sever motor disabilities using auto-adaptive Brain-Machine Interface (BMI) / neuroprosthetics. Neuroprosthetics record, and decode brain neuronal signal for activating effectors (exoskeleton, implantable spinal cord stimulator etc.) directly without physiological neural control command pass way interrupted by spinal cord injury. A set of algorithms to decode neuronal activity recorded at the level of the cerebral cortex (Electrocorticogram) using chronic WIMAGINE implants were developed at CLINATEC and tested in the frame of 2 clinical research protocols in tetraplegics in Grenoble and in paraplegics in Lausanne. The postdoctoral fellow will contribute to the next highly ambitious scientific breakthroughs addressing the medical needs of patients. The crucial improvement of usability may be achieved by alleviating the need of constant BMI decoder recalibration introducing an auto-adaptive framework to train the decoder in an adaptive manner during the neuroprosthetics self-directed use. Auto-adaptive BMI (A-BMI) adds a supplementary loop evaluating from neuronal data the level of coherence between user’s intended motions and effector actions. It may provide BMI task information (labels) to the data registered during the neuroprosthetics self-directed use to be employed for BMI decoder real-time update. Innovative A-BMI neural decoder will be explored and tested offline and in real-time in ongoing clinical trials.

Laboratory

Clinatec (LETI)
Clinatec (LETI)
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