Recognition of eyelid movement using electroencephalographic signals

Abstract

Eyelid movement patterns are a key factor in the detection of fatigue, and in this study, electroencephalography (EEG) was used to record the brainwave patterns associated with eyelid movement in subjects during various stages of fatigue. The three movements involved were no eyelid movement, closing the eye, and opening the eye. The collected signals were processed using the wavelet transform (WT) to break down the EEG signal and obtain the main features. The support vector machine (SVM) and back propagation neural network (BPNN) were used to determine eyelid movement conditions.