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The study on brain-computer interface technology to achieve the automatic control of unmanned vehicles can help people with disabilities to realize self-service travel, thus attracting more and more attention from scholars and manufacturers. In this paper, the visual evoked potentials of human brain are extracted by visual stimulator of FPGA, and the evoked potential vector by waveform matching recognition algorithm on Labview platform, which are used as the control signals of brain-computer interface to realize automatic control of unmanned vehicle. The article explains the basis of related technologies, based on which, the signal processing flow of unmanned vehicle control system is introduced. Finally, experiment on the automatic system control of unmanned vehicle based on visual evoked potentials is designed. The experiment shows that the average time for sending instructions is less than 3s, and the average correct recognition rate of instructions is higher than 90%. The present research has opened up the research on the brain-computer interface controlled unmanned vehicle field, and will have a positive effect for the ultimate realization of autonomous travel for patients with limited mobility.

We propose a Rényi's entropy based analog circuit soft fault detection method. This method extracts the entropy information from the probability density function (PDF) of the output of the circuit under test (CUT), which is sensitive to the parameters of circuits. In this method, firstly, the Lagrange multiplier method with Rényi's entropy is used to deduce PDF of the output signal. Then through the maximum likelihood estimation method, we estimate the parameter D of Rényi's entropy adaptively according to the output of CUT. Finally, the value of Rényi's entropy can be calculated using the PDF and D parameter. The divergence between the Rényi's entropy corresponding to the fault and fault free circuits is adopted to detect the fault. This method can 100% detect soft faults, including the single fault and multiple faults, without complicate models and mass of data, and also with no need of interrupting the inherent contentions of CUT. Experiments are conducted respectively on two circuits that are implemented on an actual circuit board. The effectiveness of the proposed method is demonstrated by the result of the experiment.