Common spatial pattern method for real‐time eye state identification by using electroencephalogram signals

Abstract

Cross-channel maximum and minimum are used to monitor real-time electroencephalogram signals in 14 channels. On detection of a possible change, multivariate empirical mode decomposed the last 2 s of the signal into narrow-band intrinsic mode functions. Common spatial pattern is then utilised to create discriminating features for classification purpose. Logistic regression, artificial neural network, and support vector machine classifiers all could detect the eye state change with 83.4% accuracy in <2 s. This algorithm provides a valuable improvement in comparison with a recent procedure that took about 20 min to classify new instances with 97.3% accuracy. Application of the introduced algorithm in the real-time eye state classification is promising. Increasing the training examples could even improve the accuracy of the classification analytics.