A prototype of BCI medical equipment was designed based on Neurofeedback techniques, with which a test subject was trained to self-regulate his brain waves, thus improving his attentional processes and concentration, through the identification of concentration and attention indexes extracted thanks to the Theta/Beta Ratio (TBR) and Sustained attention (SA) protocols. The prototype has two user interfaces, one of which is directed to the health professional and the other is a simple video game directed to the test subject. This prototype extracts the characteristics related to the test subject's concentration and attention and evaluates them by means of a machine learning (ML) algorithm, with which 97% effectiveness was obtained in offline tests (with database) and 97.5% effectiveness with online tests (with real-time data). Dry active electrodes of gain 2 were designed, with electromagnetic noise elimination. An acquisition card with 16 physical channels and 3 multiplexed channels for a total of 19 channels was also designed; it has an ARM-Cortex M7 MCU which controls two ADS1299 signal conditioners and performs the preprocessing. Both the active electrodes and the designed acquisition card are powered by a power supply card with a 5V unipolar output and a ±2.5V bipolar output; it is powered by two 850mA LI-ion batteries, which provides autonomy of about eight hours.
All designs and codes are copyrighted. These designs and codes are copyrighted and associated to the thesis (https://repositorio.unbosque.edu.co/bitstream/handle/20.500.12495/8026/Izquierdo_Gomez_Proyecto_Grado.pdf?sequence=1&isAllowed=y), with creative commons Shar-Alike license.