Implementation of a Classification System of EEG Signals Based on FPGA

In the field of prosthetics, different technologies have been incorporated in recent years to improve their development and control, likewise the application of Field-Programmable Gate Arrays (FPGA) related to the Biomedicine field has increased due to its flexibility to perform multiple instructions in a reduced amount of time. This paper presents the implementation of a classification system based on FPGA capable of classifying characterized data, representing an imaginary motor task and a motor task in lower extremities. A three-layer feed-forward neural network was designed in Matlab, testing different architectures to assess the performance of the classifier, using methods such as the confusion matrix and the ROC curve.