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Interface cerveau-machine : de nouvelles perspectives grâce à l'accélération matérielle

Erwan Libessart 1, 2
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : Brain-Computer Interfaces (BCI) are systems that use brain activity to control an external device. Various techniques can be used to collect the neural signals. The measurement can be invasive ornon-invasive. Electroencephalography (EEG) is the most studied non-invasive method. Indeed, EEG offers a fine temporal resolution and ease of use but its spatial resolution limits the performances of BCI based on EEG. The spatial resolution of EEG can be improved by solving the EEG inverse problem, which allows to determine the distribution of electrical sources in the brain from EEG. Currently, the main difficulty is the time needed(several hours) to compute the matrix which is used to solve the EEG inverse problem. This document describes the proposed solution to provide a hardware acceleration of the matrix computation. A dedicated electronic architecture has been implemented and tested. First results show that the proposed architecture divides the calculation time by a factor of 60 on a programmable circuit. This acceleration opens up new perspectives for EEG BCI.
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Submitted on : Wednesday, February 13, 2019 - 8:04:06 AM
Last modification on : Friday, September 25, 2020 - 3:35:57 AM
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  • HAL Id : tel-02017104, version 1


Erwan Libessart. Interface cerveau-machine : de nouvelles perspectives grâce à l'accélération matérielle. Electronique. Ecole nationale supérieure Mines-Télécom Atlantique, 2018. Français. ⟨NNT : 2018IMTA0105⟩. ⟨tel-02017104⟩



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