The Study Of Interpreting Mental State Based Eye Movement Trajectory Using SVM

Eye movement trajectory is the sequence of gaze points changing along with the time when eye is gazing the things. It reflects dynamically people's eye movements when they are reading, driving, doing sports, and so on. So it contains abundant information. At present, to interpret people's mental state from their eye movementtrajectory has become a research hotspot in applied psychology. However, in many fields about eye movement research, such as, eye movement research on reading, eye movement research on watching pictures, visual search and pattern recognition, eye movement research on traffic psychology, the application of eye movement in the study of aviation psychology, the application of eye movement in sports psychology research, and so on. The experimental method applied in the eye movement research is that, firstly, count eye indicators (such as, fixation time, eye dancing time, regression time, eye twitching latent period, follow exercise time, etc), secondly, analyze statistically the statistical results (e.g. multi-factor variance analysis), finally, the experimental results are obtained according to statistical results. Such research method only analyzes the static indicators of eye movement trajectory, and is lack of analysis of uniform dynamic indicators of eye movement trajectory.Therefore, this study surrounds closely the purpose of proposing the method of analyzing dynamic indicators of eye movement trajectory, and combines with advanced data mining technology to analyze eye movement trajectory. This paper uses 4*4 Sudoku as research material. Tobii eye tracking system records the eye movements when subjects are solving the 4*4 Sudoku. Then preprocess the recorded eye movement trajectory data with the method of weighting the integrated eye indicators, and analyze the preprocessed eye movement trajectory data with SVM from machine learning. In the end, interpret people's mental state from eye movement trajectory.The research methods in this paper can be applied in other fields, such as, analyze drivers'eye movement trajectory, and design rational traffic management rules to prevent traffic accidents; analyze people's eye movement trajectory when they are reading to design reasonable man-machine interface, and so on.In this paper, the main works are as follows:1,In order to interpret people's mental state from eye movement trajectory, this paper uses 4*4 Sudoku as research material. Tobii eye tracking system records the subjects'eye movement trajectory when they are answering 4*4 Sudoku. And then interpret people's mental state when they are solving 4*4 Sudoku from the recorded eye movement trajectory. 2,Data preprocessing is the most critical step in data mining, and it influences the quality of data mining. The traditional eye movement research analyzes statistically the single eye indicator (such as, fixation time, regression time, and so on), but can not completely reflect the eye movement information. In this paper, the preprocessing method is that, weighting the eye composite indicators of every area of interest in 4*4 Sudoku, and fully reflecting the eye movement trajectory.3,Support vector machine(SVM)as a new emerging machine learning method, with its good classification performance, attracted widespread attention, has achieved fruitful research results. This paper uses SVM to classify the eye data preprocessed, and then interprets the mental state according to the classification results.4,The 4*4 Sudoku contains seven problem solving strategies, whereas the traditional SVM is two-classification method. So this paper combines binary tree multi-classification SVM algorithm with clustering, and establishes multi-classification binary decision tree hierarchy, and achieves the multi-classification of problem solving strategies.