EOG angle recognition based on mixed LPC and DTW

Abstract

In this thesis, EOG model is divided into four groups according to different scanning Angle, which is L1, L2, R1 and R2. Feature extraction adopts mixed LPC coefficient with the EOG waveform feature. This thesis adopts DTW, SVM and BP neural network on the EOG Angle recognition, each algorithm can achieve good recognition rate, but DTW is better. This thesis also designs an application that controls cartoon character movement through eye glancing to different Angles in different directions. That app can identify the eye movement type of the current data based on the template already stored and DTW algorithm.

Keywords:

• EOG Angle recognition
• Mixed LPC< DTW