Three-dimensional Visual Cortex Evoked Potential Feature Extraction Study

Vision research is always a key region of the modern science. When we talk about our vision system, we have to notice that stereoscopic cognition is an important portion of it and any progress of the research will effect many facet of our life, such as self-control robot, image processing and bionics. During the pass time, we have taken a lot of experiments of stereoscopic cognition in psychology and physiology and attain some results, include the influence of RDS(Random Dots Stereogram) contrast, brightness, complexity of disparity area and color to the cognition. And at the same time, we reported that beside the Nl, P2 wave in CEP(Cortical Evoked Potential) , there is a probability of existing of another wave, we call N2, and it sees sensitive to disparity depth of RDS.I build my research work on the pass and improved the hardware and software of experimental system, and explored some new analyzing methods to pick out the CEP signal wrapped in the EEG more efficiently.On hardware, I designed a instrument to let our testee to observe the RDS, shown on Cathode-ray tube(CRT) or printed on paper, through a stereoscope, and never let the stereoscope effect the observation. The instrument includes 3 modules: RDS showing, stereoscope fixing and testee fitting. At the same, we examined the EEG instrument for some problems, included EEG acquisition box(3 pieces circuits), monitor system and amplifying system.On software, I designed a toolbox of experimental application and divided it into 3 part: CEP collecting application, CEP analyzing application and lab source management application. CEP collecting application included 4 main modules, AD card calibration, CEP testing, RDS showing and CEP acquisition. CEP analyzing application's main function was to extract CEP data from data files for analyzing program. The last one was a new one of our experimental system. It would improve the ability of managing the lab software and hardware resource and take the lab management into a network world.The last work was exploring the advantages of the new signal analyzing methods: wavelet and FastICA on CEP analysis. Our prior method on analyzing CEP was accumulating the CEP and taking the mean of them, and the accumulating times were about 120. In chapter 4, I performed the analyzing work with wavelet and FastICA and find that wavelet was a high efficient way to filter the CEP signal when I set the wavelet and scaling function to Haar, decomposing level to 4, threshold method to fixed to threshold and white-noise structure to scaled white-noise. It took the advantage that reduced the accumulating times to 60 with more smooth signal and less distortion. But FastICA takes no advantage on this facet.