In recent years, electroencephalography (EEG) has received increasing attention in the field of biometrics because of its unique advantages such as covertness, resistance to spoofing, sensitivity to emotional and mental states, and continuous nature. Visual evoked potentials (VEPs) have been widely used in EEG-based biometrics owing to fast recognition speed and high accuracy. This study proposes a new method to combine multiple visual stimuli for VEP-based individual identification. Correct recognition rate (CRR) was estimated using steady-state VEPs (ss-VEPs), and code modulated VEPs (c-VEPs) recorded from a group of 35 subjects. c-VEPs achieved a 100% CRR using 3.1-s of VEP data (a 10.8-s duration, including 7.7-s intervals) in the cross-session condition. An online system based on the combination of stimuli optimized from the data of 35 subjects was further developed and validated with an additional group of 22 subjects. A cross-session CRR of 99.55% was achieved using the same parameters. These results indicate that the proposed VEP-based individual identification method using multiple visual stimuli shows great potential for practical applications.

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.