## 4 Discussion
It is especially effective for modeling complex frequency domain features [42], such as the high-dimensional sub-band response features produced by FBCCA. The weighted fusion mechanism we designed goes beyond traditional majority voting. Instead, it establishes a structurally synergistic decision-making process by analyzing the performance weights of each classifier across different feature types. This approach balances discriminative power and resistance to interference, avoiding the interpretability challenges often associated with deep learning models. Compared to recent \"black-box\" deep learning-based EEG decoding methods [43], our dual-classifier structure maintains high interpretability and flexible parameter tuning, making it particularly well suited for high-reliability, asynchronous systems where minimizing false activations is critical. SVMs excel at learning geometric boundaries in the low-dimensional space shaped by FBCSPs, while XGBoost effectively handles the high-dimensional, nonlinear feature distributions derived from FBCCA. By fusing the outputs of both classifiers with learned weights, the system achieves enhanced discriminative stability in high-dimensional feature space and avoids overfitting to specific feature sets that can occur with single models. This classifier fusion represents not only an improvement in model accuracy but also an architectural optimization of the fusion strategy, highlighting the system's innovation in information integration mechanisms. Despite the encouraging results achieved in this study, several issues remain that warrant further investigation. First, the current fusion weights are empirically set and cannot dynamically adapt to real-world interaction conditions such as user state fluctuations and signal variability. Future research could explore adaptive weighting mechanisms based on reinforcement learning, Bayesian optimization, or neural architecture search, aiming to enhance the system's long-term applicability and robustness. Second, the system has not yet been deployed in a real-time asynchronous interaction environment, lacking comprehensive evaluation in terms of response latency, computational resource consumption, and user feedback. Asynchronous BCI systems are highly sensitive to response time [44]. Therefore, future efforts should focus on embedding the model into real-time systems and conducting system-level online testing. The current experimental validation was conducted only with healthy subjects, and there is a lack of application evaluation in clinical populations such as individuals with motor disorders or cognitive impairments. Given the known differences in neural response timing and activation patterns among these groups [45; 46], it is important to develop personalized EEG feature extraction strategies to improve the model's generalizability across diverse populations. Additionally, this study has not yet conducted a direct quantitative comparison of classification performance with other mainstream methods, such as TRCA or CNN-based classifiers. Therefore, in future work, we plan to integrate our fusion strategy into more advanced models to evaluate its generalizability, robustness, and applicability across different classification frameworks. These insights would provide a theoretical foundation for developing highly trustworthy, interpretable, and auditable BCI systems. In conclusion, this study demonstrates that combining multi-band EEG features (FBCSP and FBCCA) with dual classifier ensembles (SVM and XGBoost) provides a promising solution for improving asynchronous state detection in SSMVEP-BCI systems. The proposed methodology enhances feature separability and classification reliability, which is a key step in the development of practical, user-friendly, and high-performance asynchronous BCIs.