A novel eye movement detection algorithm for EOG driven human computer interface

Bio-based human computer interface (HCI) has attracted more and more attention of researchers all over the world in recent years. The paper is concerned with eye movement detection algorithm and the EOG-based human computer interface. The linear predictive coding cepstrum (LPCC) coefficients of EOG pulses are extracted as feature vectors, which are used for eye movement pattern matching. Dynamic time warping (DTW) is adopted to solve the discrepancy in EOG pulses duration among different trials. Furthermore, spectral entropy algorithm is used to detect the endpoints of EOG pulses, which can improve the robustness and increase the recognition rate in noisy background. Experimental and simulation results based on real-life EOG signals show that the proposed algorithm has stable performance and can be used for online controlling and communication in EOG based HCI system.