Steady-state visual evoked potentials (SSVEP) have become one of the most widely used EEG signals in brain-controlled robot systems due to their advantages of high signal-to-noise ratio （SNR） and high information transmission rate. However, due to the phenomenon of ′BCI blindness′, some subjects have difficulty inducing stable SSVEP responses, which limits the application of this technology in practical scenarios. To improve the recognizability of SSVEP signals, this paper proposes a novel modulation SSVEP paradigm that integrates event-related potential time series, and designs an online brain-controlled robot system based on this paradigm, which can achieve coordinated control of wheeled robots in complex scenarios. Firstly, without changing the standard SSVEP frequency characteristics, embedding event-related potential stimuli can enhance user attention and improve signal quality; then, EEG signals generated by 8 subjects under the modulation paradigm and standard paradigm were collected. By comparing their offline analysis results under the filter group canonical correlation analysis and task correlation analysis algorithms, the improvement effect of modulation paradigm on EEG signals was verified, and the algorithm with high accuracy was selected for data processing in the online system; finally, this paper develops an online brain-controlled robot system based on this paradigm and conducts online experiments. The offline analysis results show that the modulation paradigm generates EEG signals with a classification accuracy improvement of 2%~32% compared to the standard SSVEP paradigm under various classification algorithms, effectively alleviating the problem of ′BCI blindness′. The online brain-controlled robot system has shown good classification performance in multiple subjects, with an average information transmission rate （ITR） of 30.74 bits/min, proving its feasibility and effectiveness in practical applications.