Random eye state change detection in real-time using EEG signals

Abstract

Maximum and minimum computed across channels is used to monitor the Electroencephalogram signals for possible change of the eye state. Upon detection of a possible change, the last two seconds of the signal is passed through Multivariate Empirical Mode Decomposition and relevant features are extracted. The features are then fed into Logistic Regression and Artificial Neural Network classifiers to confirm the eye state change. The proposed algorithm detects the eye state change with 88.2% accuracy in less than two seconds. This provides a valuable improvement in comparison to a recent procedure that takes about 20 minutes to classify new instances with 97.3% accuracy. The introduced algorithm is promising in the real-time eye state classification as increasing the training examples would increase its accuracy.