Conventional polygraphy has several alternatives and one of them is P300-based guilty knowledge test. The purpose of this paper is to apply a new method called empirical mode decomposition (EMD) to extract features from electroencephalogram (EEG) signal. EMD is an appropriate tool to deal with the nonlinear and nonstationary nature of EEG. In the previous studies on the same data set, some morphological, frequency, and wavelet features were extracted only from Pz channel, and used for the detection of guilty and innocent subjects. In this paper, an EMD-based feature extraction was done on EEG recorded signal. Features were extracted from all three recorded channels (Pz, Cz, and Fz) for synergistic incorporation of channel information. Finally, a genetic algorithm was utilized as a tool for efficient feature selection and overcoming the challenge of input space dimension increase. The classification accuracy of guilty and innocent subjects was 92.73%, which was better than other previously used methods.

In this research, a web-based simulator is developed, which can be used for generating image sequences of moving spermatozoa cells. It can be used for assessment of multiple object tracking algorithms, especially Computer Aided Sperm Analysis (CASA) systems. The developed software has many useful parameters such as blurring images or adding noise and it also gives full control of sperm counts and types. To illustrate performance of the developed simulator, three parameters (spermatozoa population, standard deviation of Gaussian blur filter and noise intensity) have been swept and the results of three different multiple object tracking algorithms were compared as an application of this simulation.