Desenvolvimento de sistema de aquisição e visualização da onda alfa do EEG para smartphones

Abstract

The electroencephalography (EEG) signal provides various information about the human body and can be used for the diagnosis of diseases and for the development of human-machine interfaces, which apply this signal for the movement of prostheses and limb movement recovery. There are commercial EEG acquisition boards, each of which has its differential in terms of components. With the interconnection of the acquisition board to a microcontroller, it is possible to send the EEG signal to a web page, allowing the signal to be visualized in any equipment that is connected to the same network as this microcontroller, without the need to have a wired communication. This work aims to build an electroencephalography acquisition system that enables the visualization of the alpha wave on a smartphone or any other device connected to the microcontroller network (through an ESP32 controller), with the data being sent via WiFi to a web page, which plots the alpha wave of the EEG. To develop the acquisition circuit, the integrated circuit (IC) AD8232 was used to acquire biopotentials. Along with this IC, a high-pass filter, a low-pass filter, a notch filter have been designed, as well as a right leg driver circuit being integrated into the circuit to reduce common-mode interference. The acquisition system was connected to a microcontroller, which via wireless communication, sends the captured alpha wave data to be visualized in a graphic on a web page. The acquisition system had cutoff frequencies of 2.1 Hz and 12.5 Hz and a maximum gain of 57 dB. The notch filter attenuated the frequency of 60 Hz, leaving it with a magnitude of 1.4 dB. The sending system acquired the data at a sampling rate of 100 Hz and the web page requests data every 100 ms, managing to assemble the alpha wave graph without losing information and without delays. The three interconnected systems were validated by testing three volunteers, and in these results the alpha wave behavior of each individual could be visualized, managing to differentiate the alpha wave behavior when the person has their eyes open and when their eyes are closed.