Détection et Classification de Signaux Neuronaux
Abstract
The processing of a neural signal can provide a wealth of information. Knowing which neuron is excited at a given moment, we can understand to a certain extent the language of our brain. The classification of neuronal signals is therefore a key area of neuroscience, enabling us to real-time analysis of cerebral information to trace the motor actions envisaged by the subject. One example is the provision of neuro-connected prostheses for tetraplegic patients. Such an analysis can be implemented using an artificial neural network. The aim of this internship was therefore to create biologically realistic data to test the network, create a graphical interface for it and optimize it.
Cite this work
Andrea Combette (2023). Détection et Classification de Signaux Neuronaux. Internship report, École normale supérieure, Paris.
@techreport{combette2023neuralsignals,
title = {Détection et Classification de Signaux Neuronaux},
author = {Andrea Combette},
institution = {École normale supérieure, Paris},
year = {2023},
type = {Internship report}
}