Electrooculography Signals Classification for FPGA-based Human-Computer Interaction

Electrooculographic techniques are applied in the development of new technologies that compensate for the limitations of people with motor disabilities. The algorithms in charge of classifying these signals play a fundamental role, mainly for Human Computer Interfaces (HCI), specially when the machine learning algorithms are implemented in customized hardware like FPGA. In this work, electrooculography data were collected from 10 healthy subjects during six eye movement tasks. Then, the data were filtered and introduced into supervised and unsupervised learning algorithms with six classification labels. The results obtained showed that the SVM algorithm had 93.5% of accuracy, thus being considered the most efficient of the classification algorithms proposed in this work. Then, we develop a custom hardware architecture for real-time implementation of EOG classification model in al FPGA card. We demonstrate the effectiveness of the proposed framework for EOG data classification.