Brain-computer interfaces(BCI)based on steady-state visual evoked potentials(SSVEP)have attracted great interest because of their higher signal-to-noise ratio,less training,and faster information transfer.However,the ...Brain-computer interfaces(BCI)based on steady-state visual evoked potentials(SSVEP)have attracted great interest because of their higher signal-to-noise ratio,less training,and faster information transfer.However,the existing signal recognition methods for SSVEP do not fully pay attention to the important role of signal phase characteristics in the recognition process.Therefore,an improved method based on extended Canonical Correlation Analysis(eCCA)is proposed.The phase parameters are added from the stimulus paradigm encoded by joint frequency phase modulation to the reference signal constructed from the training data of the subjects to achieve phase constraints on eCCA,thereby improving the recognition performance of the eCCA method for SSVEP signals,and transmit the collected signals to the robotic arm system to achieve control of the robotic arm.In order to verify the effectiveness and advantages of the proposed method,this paper evaluated the method using SSVEP signals from 35 subjects.The research shows that the proposed algorithm improves the average recognition rate of SSVEP signals to 82.76%,and the information transmission rate to 116.18 bits/min,which is superior to TRCA and traditional eCAA-based methods in terms of information transmission speed and accuracy,and has better stability.展开更多
The working of Canonical switching cell(CSC)converter was studied and its equivalent circuit during ON and OFF states were obtained.State space model of CSC converter in ON and OFF states were developed using the Kirc...The working of Canonical switching cell(CSC)converter was studied and its equivalent circuit during ON and OFF states were obtained.State space model of CSC converter in ON and OFF states were developed using the Kirchhoff laws.The state space matrices were used to construct the transfer functions of ON&OFF states.The step response of the converter was simulated using MATLAB.The step response curve was obtained using different values of circuit components(L,C1,C2 and RL)and optimized.The characteristic parameters such as rise time,overshoot,settling time,steady state error and stability were determined using the step response curve.The response curve shows that there is no overshoot;the rise time and settling time are very low as expected for a converter and its stability is very high but the amplitude is very.The circuit was tuned to attain the expected amplitude using PID controller with the help of Genetic algorithm.The excellent results of circuits’characteristic parameters are very useful guideline for constructing such CSC converters for DC-DC conversions.The circuit characteristic parameters are useful in constructing such CSC converters for DCDC conversions in driving solar energy using solar panel.展开更多
文摘Brain-computer interfaces(BCI)based on steady-state visual evoked potentials(SSVEP)have attracted great interest because of their higher signal-to-noise ratio,less training,and faster information transfer.However,the existing signal recognition methods for SSVEP do not fully pay attention to the important role of signal phase characteristics in the recognition process.Therefore,an improved method based on extended Canonical Correlation Analysis(eCCA)is proposed.The phase parameters are added from the stimulus paradigm encoded by joint frequency phase modulation to the reference signal constructed from the training data of the subjects to achieve phase constraints on eCCA,thereby improving the recognition performance of the eCCA method for SSVEP signals,and transmit the collected signals to the robotic arm system to achieve control of the robotic arm.In order to verify the effectiveness and advantages of the proposed method,this paper evaluated the method using SSVEP signals from 35 subjects.The research shows that the proposed algorithm improves the average recognition rate of SSVEP signals to 82.76%,and the information transmission rate to 116.18 bits/min,which is superior to TRCA and traditional eCAA-based methods in terms of information transmission speed and accuracy,and has better stability.
文摘The working of Canonical switching cell(CSC)converter was studied and its equivalent circuit during ON and OFF states were obtained.State space model of CSC converter in ON and OFF states were developed using the Kirchhoff laws.The state space matrices were used to construct the transfer functions of ON&OFF states.The step response of the converter was simulated using MATLAB.The step response curve was obtained using different values of circuit components(L,C1,C2 and RL)and optimized.The characteristic parameters such as rise time,overshoot,settling time,steady state error and stability were determined using the step response curve.The response curve shows that there is no overshoot;the rise time and settling time are very low as expected for a converter and its stability is very high but the amplitude is very.The circuit was tuned to attain the expected amplitude using PID controller with the help of Genetic algorithm.The excellent results of circuits’characteristic parameters are very useful guideline for constructing such CSC converters for DC-DC conversions.The circuit characteristic parameters are useful in constructing such CSC converters for DCDC conversions in driving solar energy using solar panel.
