The electroencephalography (EEG) method has recently attracted increasing attention in the study of brain activity-based biometric systems because of its simplicity, portability, noninvasiveness, and relatively low cost. However, due to the low signal-to-noise ratio of EEG, most of the existing EEG-based biometric systems require a long duration of signals to achieve high accuracy in individual identification. Besides, the feasibility and stability of these systems have not yet been conclusively reported, since most studies did not perform longitudinal evaluation. In this paper, we proposed a novel EEG-based individual identification method using code-modulated visualevoked potentials (c-VEPs). Specifically, this paper quantitatively compared eight code-modulated stimulation patterns, including six 63-bit (1.05 s at 60-Hz refresh rate) m-sequences (M1-M6) and two spatially combined sequence groups (M×4: M1-M4 and M× 6: M1-M6) in recording the c-VEPs from a group of 25 subjects for individual identification. To further evaluate the influence of inter-session variability, we recorded two data sessions for each individual on different days to measure intra-session and cross-session identification performance. State-of-the-art VEP detection algorithms in brain-computer interfaces (BCIs) were employed to construct a template-matching-based identification framework. For intra-session identification, we achieved a 100% correct recognition rate (CRR) using 5.25-s EEG data (average of five trials for M5). For cross-session identification, 99.43% CRR was attained using 10.5-s EEG signals (average of ten trials for M5). These results suggest that the proposed c-VEP based individual identification method is promising for real-world applications.

Gaze interaction provides an efficient way for users to communicate and control in virtual reality (VR) presented by head-mounted displays. In gaze-based text-entry systems, eye tracking and brain-computer interface (BCI) are the two most commonly used approaches. This paper presents a hybrid BCI system for text entry in VR by combining steady-state visual evoked potentials (SSVEP) and eye tracking. The user interface in VR designed a 40-target virtual keyboard using a joint frequency-phase modulation method for SSVEP. Eye position was measured by an eye-tracking accessory in the VR headset. Target-related gaze direction was detected by combining simultaneously recorded SSVEP and eye position data. Offline and online experiments indicate that the proposed system can type at a speed around 10 words per minute, leading to an information transfer rate (ITR) of 270 bits per minute. The results further demonstrate the superiority of the hybrid method over single-modality methods for VR applications.