A Study of Electroencephalogram and Electrodermal Signals in the
Reinforcement Learning for a Brain Machine Interface
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The aim of this work is to build an intelligent control system for a brain-machine interface, using the Artificial Neural Networks paradigm. The interface built translates brain signals to move a cursor on a digital screen. The control system uses a feedback signal from the user to calibrate itself, allowing it to adjust the movement of the cursor in a personalized way, according to the signals sent by the user. By using artificial neural networks, we were able to reduce the training time, which in traditional control systems can take two to three months, to around five minutes. The project ai...
The aim of this work is to build an intelligent control system for a brain-machine interface, using the Artificial Neural Networks paradigm. The interface built translates brain signals to move a cursor on a digital screen. The control system uses a feedback signal from the user to calibrate itself, allowing it to adjust the movement of the cursor in a personalized way, according to the signals sent by the user. By using artificial neural networks, we were able to reduce the training time, which in traditional control systems can take two to three months, to around five minutes. The project aims to facilitate accessibility for individuals with limited physical and motor abilities, whether temporary or permanent. The construction of a system that translates limbic signals into digital responses makes it possible to develop a range of new applications to increase the autonomy of people with motor limitations. As a continuation of this work, many applications could be developed for home automation of basic tasks, such as turning on a light or a household appliance
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