Application of TimeScale Decomposition of Entropy for Eye Movement Analysis The methods for nonlinear time series analysis were used in the presented research to reveal eye movement signal characteristics. Three measures were used: approximate entropy, fuzzy entropy, and the Largest Lyapunov Exponent, for which the multilevel maps (MMs), being their timescale decomposition, were defined. To check whether the estimated characteristics might be useful in eye movement events detection, these structures were applied in the classification process conducted with the usage of the kNN method. The elements of three MMs were used to define feature vectors for this process. They consisted of differently combined MM segments, belonging either to one or several selected levels, as well as included values either of one or all the analysed measures. Such a classification produced an improvement in the accuracy for saccadic latency and saccade, when compared with the previously conducted studies using eye movement dynamics.

Detection Model of Depression Based on Eye Movement Trajectory Eye movement trajectories of depressed patients and normal persons are different. The eyetracking data obtained by the eye tracker can adequately summarize the characteristics of the eye movement trajectory. Based on the characteristics of eye movement trajectory, this paper proposes a new depression detection model by using an artificial neural network, which can better assist doctors in the diagnosis of depression. First, we extract the feature of eye movement trajectory, which obtains from timeseries data recording the trajectory of the eye. Then, we convert the data from threedimensional to twodimensional, and perform feature extraction and transformation. Finally, we propose a new depression detection model by using artificial neural networks. The experimental results show that the best result of the model evaluation is 83.17%, which can effectively assist doctors in the diagnosis of depression.