## 1 Introduction
In the context of SSMVEP-based asynchronous state detection, the core assumption is that NC-state EEG lacks pronounced frequency components corresponding to stimulus frequencies, whereas IC-state EEG exhibits clear responses at stimulus frequencies and their harmonics [23]. Therefore, a hybrid feature extraction strategy that integrates frequency domain correlation and spatial energy distribution is desirable. Canonical correlation analysis is particularly effective for quantifying the similarity between EEG signals and reference templates across multiple frequencies, requiring no training and offering high efficiency for real-time applications [24]. In parallel, the common spatial pattern (CSP) algorithm can identify spatial filters that maximize the variance difference between IC and NC states, enabling the extraction of discriminative spatial features [25]. Motivated by these insights, this study proposes a dual-feature fusion framework combining CCA and CSP-derived features, designed to capture both frequency and spatial domain information. Moreover, different classifiers exhibit varying degrees of compatibility with different types of features [26]. SVMs have been widely applied to motor-related EEG classification tasks, showing robust performance in high-dimensional, sparse, and linearly separable data contexts [27]. In contrast, extreme gradient boosting (XGBoost) has demonstrated strong capability in modeling nonlinear relationships and handling redundant features, making it well suited for complex EEG tasks such as seizure detection and emotion recognition [28,29]. The features extracted by FBCSPs and FBCCA differ substantially in their statistical characteristics, representational structures, and dimensional profiles. FBCSPs extract channel-wise variance through linear spatial filtering, producing relatively low-dimensional features with a linear structure. These properties make FBCSP features well suited for boundary-based classifiers such as SVMs [30]. In contrast, FBCCA generates high-dimensional features by measuring frequency domain correlation with reference templates. These features are inherently nonlinear and more complex in structure, aligning well with XGBoost, which excels in nonlinear modeling and embedded feature selection [31]. The direct concatenation of these heterogeneous feature sets into a single classifier could introduce issues such as dimensionality mismatch, overfitting, and compromised generalization. To address this, the proposed framework adopts a structured feature-classifier pairing approach: the SVM is paired with FBCSP features, and XGBoost is paired with FBCCA features. This strategy leverages the strengths of each classifier in handling its respective feature type. A weighted probabilistic fusion mechanism is then employed to integrate the outputs from the two classifiers. This fusion enables the system to exploit the complementary strengths of both classifiers without diminishing discriminative capability. By employing this dual-branch classifier fusion scheme--an SVM for FBCSPs and XGBoost for FBCCA--the framework achieves both classifier-specific adaptability and coordinated optimization of feature-model compatibility, thereby enhancing robustness and generalization in asynchronous BCI state detection. In summary, this study proposes a dual-level fusion framework for asynchronous SSMVEP-based BCI systems, incorporating a three-tier collaborative structure that integrates heterogeneous features, heterogeneous classifiers, and decision-level fusion. At the feature level, spatial-domain and frequency domain features are extracted using FBCSP and FBCCA, enabling joint modeling of spatial distributions and frequency-locked responses in EEG signals. At the classifier level, SVM and XGBoost are respectively employed to process the FBCSP and FBCCA features, and their outputs are combined using a weighted probabilistic fusion strategy to achieve structured feature-classifier alignment and collaborative optimization.