Brain–computer interfaces (BCIs) are a new technology that subverts traditional human–computer interaction, where the control signal source comes directly from the user’s brain. When a general BCI is used for practical applications, it is difficult for it to meet the needs of different individuals because of the differences among individual users in physiological and mental states, sensations, perceptions, imageries, cognitive thinking activities, and brain structures and functions. For this reason, it is necessary to customize personalized BCIs for specific users. So far, few studies have elaborated on the key scientific and technical issues involved in personalized BCIs. In this study, we will focus on personalized BCIs, give the definition of personalized BCIs, and detail their design, development, evaluation methods and applications. Finally, the challenges and future directions of personalized BCIs are discussed. It is expected that this study will provide some useful ideas for innovative studies and practical applications of personalized BCIs.

Light and noise are important factors affecting shooting performance, and shooters can exhibit physiological processes that differ from normal shooting when they are subjected to disturbed visual and auditory conditions. The purpose of this study was to explore the neural mechanism of shooting preparation in skilled shooters with visual and auditory limitations. We designed an experiment and recorded the electroencephalograph (EEG) and shooting performance indexes of 40 individuals skilled in marksmanship during the shooting preparation stage under three conditions: low light, noise interference, and a normal environment. EEG relative band power features and event-related desynchronization/synchronization (ERD/ERS) features were extracted and analyzed. The results showed that (1) the average score of the shooters was 8.55 under normal conditions, 7.71 under visually restricted conditions, and 8.50 under auditorily restricted conditions; (2) the relative EEG band power in the frontal lobe (Fp1, Fp2), frontal lobe (F4, F8), left temporal region (T7), central lobe (CP2), and parietal lobe (P3, PO3) in the theta band was significantly lower than in the other two environments (p < 0.05), and there was no significant difference between the power intensity of the shooter in the noisy environment and that in the normal environment; and (3) in the low-light environment, a significant negative correlation was found between the central region, the left and right temporal regions, and the parietal lobe (p < 0.05). These findings provide a basis for further understanding neural mechanisms in the brain during the shooting preparation phase under visually and auditorily restricted conditions.

The steady-state visually evoked potential (SSVEP) is an important type of BCI that has various potential applications, including in virtual environments using virtual reality (VR). However, compared to VR research, the majority of visual stimuli used in the SSVEP-BCI are plane stimulation targets (PSTs), with only a few studies using stereo stimulation targets (SSTs). To explore the parameter optimization of the SSVEP-BCI virtual SSTs, this paper presents a parameter knowledge graph. First, an online VR stereoscopic stimulation SSVEP-BCI system is built, and a parameter dictionary for VR stereoscopic stimulation parameters (shape, color, and frequency) is established. The online experimental results of 10 subjects under different parameter combinations were collected, and a knowledge graph was constructed to optimize the SST parameters. The best classification performances of the shape, color, and frequency parameters were sphere (91.85%), blue (94.26%), and 13Hz (95.93%). With various combinations of virtual reality stereo stimulation parameters, the performance of the SSVEP-BCI varies. Using the knowledge graph of the stimulus parameters can help intuitively and effectively select appropriate SST parameters. The knowledge graph of the stereo target stimulation parameters presented in this work is expected to offer a way to convert the application of the SSVEP-BCI and VR.

Background: Visual imagery (VI) is a cognitive process that generates visual representations in the absence of external visual input; however, the spatiotemporal organization of its neural representations remains poorly understood. Methods: In this study, we combined electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) with representational similarity analysis (RSA) and the Schaefer 2018 functional parcellation atlas to compare the correspondence between brain network representations and CORnet-S model representations during visual perception (VP) and VI. We further examined the cross-modal associations between EEG time–frequency representations and fMRI network representations during VI. Results: Significant model–brain representational correspondence was detected in the visual network during VP, and no significant correspondence was detected in any network during VI, yielding different descriptive significance profiles across the two tasks. Cross-modal EEG–fMRI RSA further revealed frequency- and time-dependent representational correspondence involving the frontoparietal, default mode, ventral attention, visual, and limbic networks. Significant cross-modal correspondence was observed across the alpha, beta, and gamma bands, with descriptive variation in the networks and temporal clusters involved in each frequency band. Conclusions: Collectively, these findings suggest that VI is not merely a reinstatement of VP but is associated with distributed representational patterns involving multiple functional networks. This study provides multimodal evidence for understanding the neural mechanisms underlying VI.