Implementation of Closing Eyes Detection with Ear Sensor of Muse EEG Headband using Support Vector Machine Learning

Abstract

Epilepsy patients might need continuous electroencephalography (EEG) monitoring to help them understand their own condition’s improvements or their doctors to determine the frequency of the seizures. In this study, we demonstrated how a low-cost, portable EEG headband could be used to detect absence seizures in epilepsy patients. The method used is Support Vector Machine (SVM) to separate the initial limit and Machine Learning with Tensorflow to predict its confidence level. Then, we tried to test our method on 2 other patients to see its measurement divergence. Furthermore, the Tensorflow library has been applied and developed to train and classify the data, which is also one of the novelty approaches in this study. From the result, closed eyes can be detected from open-eye conditions with more than 96 % accuracy and a loss of only 2.35%. The findings demonstrate the feasibility of detecting absence seizures using only two electrodes which were TP9 and TP10 of Muse headband which is also positioned in the ear like an ear-EEG. Overall, the study successfully developed a novel method utilizing a low-cost headband to provide affordable health system access.