An EOG signals recognition method based on improved threshold dual tree complex wavelet transform

Human Machine Interface (HMI) system can effectively detect Electrooculogram (EOG) signals of eye movements, extract intension of users, and convert them into control commands of computer or rehabilitation aid devices. Thus, HMI system is easily accepted by the majority of persons with disabilities. Discrete Wavelet Transform (DWT) method was mainly used in feature extraction of EOG signals, but Traditional DWT method was always suffered from severe frequency aliasing and poor shift invariant. In this paper, Dual-tree Complex Wavelet Transform (DTCWT) with a novel threshold calculation method was proposed for feature selection of EOG signals. To verify the proposed method, The EOG signal was collected from 5 normal subjects in the laboratory, and featured selected. Then, Support Vector Machine (SVM) was applied for classification. The average correct detection rate of proposed method was 96.11%, which was higher than Traditional DWT method. These results demonstrate that the DTCWT-SVM algorithm provides high classification accuracy, and suitable for clinical medicine field.