Eyelid Movement Command Classification Using Machine Learning.

Abstract

The Eyelid Drive System (EDS) is an assistive technology device intended to allow users to wirelessly control other devices, such as power wheelchairs and personal computers, using commands consisting only of blinking and winking. In this paper, four machine learning classifiers are trained on data taken from one subject and validated offline on the training subject plus two additional subjects. The classifiers are assessed for accuracy, computational and memory requirements, and transferability from the "training" subject to the other two subjects. A support vector machine (SVM) achieved the highest level of accuracy (97.5%) while using a potentially prohibitive level of computational and memory resources. A logistic regression classifier also achieved excellent accuracy (96.5%) while using two to three orders of magnitude fewer computational and memory resources than the SVM.