EEG signals could reveal unique information of an individual's brain activities. They have been regarded as one of the most promising biometric signals for person identification and verification. Steady-State Visual Evoked Potentials (SSVEPs), as EEG responses to visual stimulations at specific frequencies, could provide biometric information. However, current methods on SSVEP biometrics with hand-crafted power spectrum features and canonical correlation analysis (CCA) present only a limited range of individual distinctions and suffer relatively low accuracy. In this paper, we propose convolution neural networks (CNNs) with raw SSVEPs for person identification and verification without the need for any hand-crafted features. We conduct a comprehensive comparison between the performance of CNN with raw signals and a number of classical methods on two SSVEP datasets consisting of four and ten subjects, respectively. The proposed method achieved an averaged identification accuracy of 96.8%�0.01, which outperformed the other methods by an average of 45.5% (p-value <; 0.05). In addition, it achieved an averaged False Acceptance Rate (FAR) of 1.53%�0.01 and True Acceptance Rate (TAR) of 97.09%�0.02 for person verification. The averaged verification accuracy is 98.34% � 0.01, which outperformed the other methods by an average of 11.8% (p-value <; 0.05). The proposed method based on deep learning offers opportunities to design a general-purpose EEG-based biometric system without the need for complex pre-processing and feature extraction techniques, making it feasible for real-time embedded systems.