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In code-modulated visual evoked potential (c-VEP) based BCI systems, flickering visual stimuli may result in visual fatigue. Thus, we introduced a discrete-interval binary sequence (DIBS) as visual stimulus modulation, with its power spectrum optimized to emphasize high-frequency components (40 Hz-60 Hz). 8 and 17 subjects participated, respectively, in offline and online experiments on a 4-target asynchronous c-VEP-based BCI system designed to realize a high positive predictive value (PPV), a low false positive rate (FPR) during idle states, and a high true positive rate (TPR) in control states, while minimizing visual fatigue level. Two visual stimuli modulations were introduced and compared: a maximum length sequence (m-sequence) and the high-frequency discrete-interval binary sequence (DIBS). The decoding algorithm was compared among the canonical correlation analysis (CCA), the task-related component analysis (TRCA), and two approaches of sub-band component weight calculation (the traditional method and the proportional method) for FBCCA and FBTRCA. In the online experiments, the average PPV, FPR and TPR achieved, respectively [Formula: see text], [Formula: see text], [Formula: see text] with m-sequence, while [Formula: see text], [Formula: see text] and [Formula: see text] with DIBS. Estimated by objective eye-related metrics and a subjective questionnaire, the visual fatigue in DIBS cases is significantly smaller than that in m-sequence cases. In this study, the feasibility of a novel modulation approach for visual fatigue reduction was proved in an asynchronous c-VEP system, while maintaining comparable performance to existing methods, which provides further insights towards enhancing this field's long-term viability and user-friendliness.

• The continuous SSVEP-BCI faces long transition time in target switching process. • The spontaneous saccade event can be used an additional information in SSVEP-BCI. • The proposed hybrid BCI combines SSVEP and saccade by Bayesian approach. • The proposed hybrid BCI diminishes transition time without increasing task burden. Brain-computer interfaces (BCIs) have emerged as one of the most promising techniques for individuals with motor disabilities to control external devices directly without peripheral pathways. While the discrete control strategy is widely adopted in BCI systems, the continuous control strategy provides intuitive and fluent control of robotic devices. However, the steady-state visual evoked potential (SSVEP)-based BCI is still far from practical applications of continuous control. One of the existing challenges is the long erroneous transition time, i.e., the duration from when the subject switches towards a new target to when the BCI system recognizes this new target. Therefore, this study proposed a hybrid BCI combining SSVEP and electrooculography (EOG). First, the prior probability distribution of the intended target was obtained by the SSVEP detection method. Different from directly picking the one with the maximum probability as the conventional SSVEP-based BCI did, the captured saccade event in the EOG signal served as an additional event to optimize this probability distribution, and the target was then extrapolated and outputted. An offline experiment simulating the target switching process and an online experiment of virtual wheelchair control were designed and conducted. The proposed hybrid BCI significantly outperformed the conventional SSVEP-based BCI in both the offline experiment (continuous accuracy:64.99 ± 3.44 % vs. 55.76 ± 3.67 %, transition time: 1.19 ± 0.073 s vs. 1.65 ± 0.08 s) and the online wheelchair control experiment (success rate: 81.67 ± 5.75 % vs. 16.67 % ± 3.76 %; collision times with the dynamic obstacle: 1.50 ± 0.50 vs. 8.25 ± 0.37; minimum distance to dynamic obstacle: 1.41 ± 0.13 m vs. 0.31 ± 0.08 m). The results revealed that by combining the SSVEP and EOG signals, the proposed hybrid BCI can significantly diminish the transition time in SSVEP-based BCI and achieve continuous, fluent control. The present study provides a novel framework for integrating the SSVEP and EOG, and promotes the potential applications of continuous brain-actuated robotic devices. [ABSTRACT FROM AUTHOR]