Mathematical analysis of electroencephalography applied to control brain machine interfaces

The acquisition of biopotentials can be seen as a very important practice in the search for understanding biological systems. Recent advances in biopotential analysis, such as electroencephalography (EEG) signals, allow us to construct new brain-machine interfaces (BMI), capable of offering alternative solutions for disabled individuals.Once it is possible to identify some brainwave patterns, such as an individual blinking or with eyes closed, this study proposes to acquire EEG signals of individuals in these two situations, using a digital signal processor and a firmware, for their processing and analysis. Therefore, the measurements were carried out using an OpenBCI\\textsuperscript{TM} Ganglion GS board. The software performs digital filtering of the acquired data and analyzes them in the time and frequency domains. This analysis enables the identification of brainwave patterns associated with eye movement. From these results, future work might apply the solutions in the acquisition of EEG signals and BMIs, using neural networks such as extreme learning machines.