A Wireless EEG Acquisition Platform based on Embedded Systems

This paper proposes a wireless EEG acquisition platform based on Open Multimedia Architecture Platform (OMAP) embedded system. A high-impedance active dry electrode was tested for improving the scalp- electrode interface. It was used the sigma-delta ADS1298 analog-to-digital converter, and developed a “kernelspace” character driver to manage the communications between the converter unit and the OMAP’s ARM core. The acquired EEG signal data is processed by a “userspace” application, which accesses the driver’s memory, saves the data to a SD-card and transmits them through a wireless TCP/IP-socket to a PC. The electrodes were tested through the alpha wave replacement phenomenon. The experimental results presented the expected alpha rhythm (8-13 Hz) reactiveness to the eyes opening task. The driver spends about 725 μs to acquire and store the data samples. The application takes about 244 μs to get the data from the driver and 1.4 ms to save it in the SD-card. A WiFi throughput of 12.8Mbps was measured which results in a transmission time of 5 ms for 512 kb of data. The embedded system consumes about 200 mAh when wireless off and 400 mAh when it is on. The system exhibits a reliable performance to record EEG signals and transmit them wirelessly. Besides the microcontroller-based architectures, the proposed platform demonstrates that powerful ARM processors running embedded operating systems can be programmed with real-time constrains at the kernel level in order to control hardware, while maintaining their parallel processing abilities in high level software applications.This work was supported by FCT with the reference project FCOMP 01 0124-FEDER-010909 (FCT/PTDC/SAU-BEB/100392/2008), FCOMP 01 0124 FEDER 021145 (FCT/PTDC/SAU- ENB/118383/2010) and by FEDER funds through the Programa Operacional Fatores de Competitividade – COMPETE and National Funds through FCT – Fundação para a Ciência e Tecnologia with the reference Project: FCOMP-01- 0124-FEDER-022674. This work is also supported by ADI Project "DoIT - Desenvolvimento e Operacionalização da Investigação de Translação" (project nº 13853, PPS4-MyHealth), funded by Fundo Europeu de Desenvolvimento Regional (FEDER) through the Programa Operacional Factores de Competitividade (POFC)