## 4 Discussion  
This study addresses the challenge of reliably distinguishing between IC and NC states in asynchronous BCIs. It proposes a hybrid SSMVEP-BCI system that integrates multidimensional features with a structurally complementary classification strategy. The system combines FBCSPs and FBCCA to jointly extract spatial and frequency information from EEG signals, effectively enhancing inter-class separability during the feature modeling phase. For classification, the SVM and XGBoost are fused with weighted integration to improve both the discriminative power and stability of the model. Experimental results demonstrate that under asynchronous conditions, the proposed system significantly outperforms methods based on single-feature extraction or traditional classification architectures, validating its effectiveness and practicality. FBCSPs and FBCCA represent two fundamental processing pathways for extracting information from EEG signals: spatial patterns and frequency features, respectively. FBCSPs, which are derived from the CSP method, apply spatial filtering to maximize the covariance difference between two classes. This reflects category-specific differences in the spatial distribution of cortical activity and is particularly effective for capturing cooperative patterns across multiple channels in tasks such as attentional shifts or motor imagery [32; 33]. FBCCA, on the other hand, is based on CCA, which assumes that the brain exhibits phase-locked responses at specific frequencies to periodic external stimuli (e.g., SSVEPs/SSMVEPs). It extracts neural coherence features by maximizing the correlation between EEG signals and reference sinusoidal sequences [34; 35]. These two feature types model distinct neural mechanisms: FBCSPs focus on spatial distribution patterns, while FBCCA emphasizes frequency-locked coupling. They are not redundant but rather represent highly complementary neural encoding pathways. Moreover, both FBCSPs and FBCCA employ a filter bank strategy to decompose and reconstruct signals across multiple frequency sub-bands. This approach helps mitigate the distribution instability caused by individual frequency drift and channel jitter, and it has become a key trend in recent steady-state visual evoked potential (SSVEP) research [36; 37]. By integrating features from different frequency bands and decoding pathways, this study achieves a robust and biologically grounded feature representation. This enriched signal foundation enhances the discrimination of complex IC/NC states in asynchronous tasks and improves resistance to noise. The fusion of FBCSPs and FBCCA enables synergistic modeling of the multichannel encoding characteristics in SSMVEP signals, enhancing the structural representation of neural information. Visualization results demonstrate superior inter-class separability for the fused features, supporting our modeling hypothesis regarding the spatial-frequency coupling mechanism. This study, based on the fusion idea of the complementarity of neural processing paths, has advantages in terms of physiological rationality and model interpretability. In terms of classifier design, this study adopts the principle of feature structure-classifier preference matching to construct a dual-classifier framework integrating an SVM and XGBost. The SVM, which is grounded in the theory of margin maximization, demonstrates a strong generalization ability when dealing with structured, low-dimensional, and boundary-concentrated data. It is particularly suited for spatially projected features that are linearly or nearly linearly separable [38]. In EEG signal processing, SVMs are widely used for classification tasks, such as motor imagery and SSVEP, due to their robustness and resistance to overfitting in high-dimensional spaces [39; 40]. In contrast, XGBoost, as a representative of ensemble tree models, excels in handling nonlinear, high-dimensional data with strong feature interdependencies [41].