Hardware and Software for Integrating Brain-Computer Interface with Internet of Things

This work shows a system that appropriately integrates a Brain–Computer Interface and an Internet of Things environment based on eye state identification. The Electroencephalography prototype for brain electrical signal acquisition has been designed by the authors. This prototype uses only one electrode and its size is very small, which facilitates its use for all type of applications. We also design a classifier based on the simple calculation of a threshold ratio between alpha and beta rhythm powers. As shown from some experiment results, this threshold-based classifier shows high accuracies for medium response times, and according to that state identification any smart home environment with those response requirements could correctly act, for example ON–OFF switching room lights.