Resumen

MARCOS ROJAS, Carlos Luis; CHAILLOUX PEGUERO, Juan David  y  ALBA BLANCO, Emiliano. Real time identification of motor imagery actions on EEG signals. EAC [online]. 2020, vol.41, n.1, pp. 101-117.  Epub 09-Mar-2020. ISSN 1815-5928.

Brain Computer Interfaces (BCI) processing algorithms need powerful computational devices to perform in real time. In this work, a hardware efficient design for the classification of Electroencephalography (EEG) signals representative of two motor imagery task is proposed and implemented on Field Programmable Gate Array (FPGA). Wavelet Packets Decomposition (WPD) is used as feature extraction algorithm and Linear Discriminant Analysis (LDA) as the classifier. The system was designed using System Generator and it was implemented on a Zybo board using Hardware/Software Co-Simulation. Simulation results show an accuracy of 80% during the classification of two motor imagery tasks, a latency of 7.5 ms for a clock frequency of 1.5 MHz, and a power consumption of 0.102 W. In addition, the amount of FPGA resources employed is less than previous similar works, proving that the design system not only achieves a good accuracy but it also does it in an efficient way.

Palabras clave : BCI; Wavelet; LDA; EEG; FPGA.

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