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Study on Implementation Technology of Visual Stimulation Based Brain-Computer Interface

Author: ChengGuangHui
Tutor: ShiRui
School: Chongqing University
Course: Computer Software and Theory
Keywords: Brain_computer interface(BCI) Visual evoked potential(VEP) DirectX Windows timer Fuzzy Recognition
CLC: TP273.5
Type: Master's thesis
Year: 2006
Downloads: 225
Quote: 7
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Abstract


Brain Computer Interface (BCI) is the system that can realize the control and communication between human brain and computer or other electronic equipment by using bioelectric signal of human brain. BCI research has drawn attention of scientists in the brain-science, Calculator engineering, biomedical engineering and human machine automatic control.The principle of the VEP based BCI was explained, Design the project of brain-computer interface(BCI) based on visual evoked potential(VEP). The critical part of the solution is the realization of the visual stimulator which provides multiple stimulation patterns for user’s characters input.The main work and the research results of this thesis contain following the aspects: Firstly, Multiple visual stimulation patterns were produced on the computer screen through programming.For the sake of the high-speed and smooth manifestation of the assurance target picture, the author adopted the technique of DirectX; While settling according to the Windows precision, the author enumerated the method for the millisecond class to settle, on the experience foundation, machine while selecting by examinations the multimedia to settle; Has been checked the data, the usage WinIo2.0 function database, solved successfully combine output.When the visual stimulator runs, the images in the windows move smoothly, stably and simultaneously, transmit data to parallel port in the real-time, Recognizable VEP signal with distinct characteristics can be evoked effectively.Secondly, A VEP based BCI experimental system was set up using two computers and the Active One biopotential measurement system.Adopt averaging method to extract the poor VEP signal from strong noises, it could improve signal/noise ratio effectively, but could not distill notability VEP signal. the task group adopt averaging method, combining with wavelet time-frequency filter. Experiment results showed 15-25 trials are needed to extract the VEP signal with distinguishable features, which may improve the communication accuracy and speed of brain of brain-computer interface.The method for translating BCI control signal were adopt. They are

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