Recently, translating a steady-state visual-evoked potential (SSVEP)-based brain-computer interface (BCI) from laboratory settings to real-life applications has gained increasing attention. This study systematically tests the signal quality of SSVEP acquired by a mobile electroencephalogram (EEG) system, which features dry electrodes and wireless telemetry, under challenging (e.g. walking) recording conditions. Empirical results of this study demonstrated the robustness of canonical correlation analysis (CCA) to movement artifacts for SSVEP detection. This demonstration considerably improves the practicality of real-life applications of mobile and wireless BCI systems for users actively behaving in and interacting with their environments.