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Thesis
Home   /   Post Doctorat   /   Development of an Incremental ANNs for efficient adaptive neuroprosthetics

Development of an Incremental ANNs for efficient adaptive neuroprosthetics

Artificial intelligence & Data intelligence Computer science and software Engineering sciences Technological challenges

Abstract

Nearly 746,000 people sustain a spinal cord injury each year, resulting in impairment or even complete loss of motor functions. Brain-Machine Interfaces (BMIs) translate brain neural signals into commands to external effectors, giving patients control over orthoses, prostheses, or even their own limbs through electrical stimulation. The project aims to develop an adaptive real-time decoding model to control the arm movements of the exoskeleton developed at Clinatec. To achieve this goal, the two main objectives of the post-doctorate are: firstly, to develop incremental machine learning for an Adaptive-BMI (Ada BMI) framework to enable real-time updating of the decoding model, and secondly, to improve the decoder's performance by extracting more informative features such as cross-frequency coupling. These improvements will lead to a decrease in training session time and thus improve patient comfort. This subject lies at the interface of three laboratories of the CEA: CEA-LIST, Clinatec, and Neurospin.

Laboratory

Département Systèmes et Circuits Intégrés Numériques (LIST)
DSCIN
Laboratoire Intelligence Intégrée Multi-capteurs
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