FPGA-Targeted Neural Architecture for Embedded Alertness Detection

Several recent works have used neural networks to discriminate vigilance states in humans from electroencephalographic (EEG) signals. Our study aims at being more exhaustive. It takes into account various connectionist models, and it precisely studies their features and their performances. Physicians have been associated to the project, especially when tuning our models. Above all, our work has been oriented in such a way to get a light, low-power, easy to wear system. First implementation works have focused on the use of a Self-Organizing Map architecture, since the most efficient neural model of our study, a multilayer perceptron (MLP), was too huge for a straightforward FPGA implementation. In this paper, we describe how the theory of FPNA (Field Programmable Neural Arrays) has been applied to this model, so as to simplify the topology of the MLP of our application. Thanks to this simplification, a fully parallel FPGA implementation has been made possible and efficient, without any significant performance loss.