## 1 Introduction  
Brain-computer interface (BCI) technology is a rapidly evolving interdisciplinary field that enables direct communication between the central nervous system (CNS) and external devices, bypassing traditional neuromuscular pathways [1, 2]. By decoding brain signals into interpretable control commands, BCI systems have been widely applied in human-machine interaction, neurorehabilitation, and communication support for individuals with severe motor impairments [3, 4, 5]. Among various BCI paradigms, steady-state visual evoked potentials (SSVEPs) have been widely adopted due to their high signal-to-noise ratio (SNR), low training requirements, and strong cross-subject consistency [6, 7, 8]. In SSVEP-based systems, users focus on flickering visual stimuli to evoke frequency-specific EEG responses for multi-target selection. However, prolonged exposure to high-frequency flickering stimuli can induce visual fatigue, reducing user comfort and long-term usability [9]. To improve user experience, researchers have proposed the steady-state motion visual evoked potential (SSMVEP)paradigm. This approach uses periodic motion stimuli--such as expanding or contracting rings--instead of flashing lights, preserving the frequency-specific response while improving visual comfort [10]. Previous studies have shown that SSMVEPs are more suitable for long-term use, offering better user acceptance and greater signal stability in practical environments [11]. In terms of operational mode, BCI systems can be categorized into synchronous and asynchronous types [12]. Synchronous BCIs require users to issue commands within predefined temporal windows, which simplifies signal processing but limits user autonomy and interaction flexibility [13]. In contrast, asynchronous BCIs allow users to initiate commands at any arbitrary time, aligning more closely with natural interaction. However, such systems must continuously monitor EEG signals to distinguish between intentional control (IC) and non-control (NC) states in real time--a technically demanding task in real-world scenarios [14; 15; 16]. State detection methods for asynchronous BCIs can be broadly divided into threshold-based and classifier-based approaches [17]. Threshold-based methods, such as canonical correlation analysis (CCA), are efficient but sensitive to individual variability and environmental noise, which may degrade performance [18; 19]. Recently, more studies have introduced supervised learning classifiers to enhance system adaptability and robustness. Existing methods have explored convolutional neural networks [20], complex network analysis [21], and multimodal feature fusion techniques [22]. For example, [PERSON] et al. [20] proposed an FFT-CNN architecture that achieved improved accuracy in asynchronous SSVEP classification, but the model was prone to overfitting due to its high complexity. [PERSON] et al. [21] employed filter banks and an optimized complex network (OCN) to extract attention-related features from multiband EEG data, followed by classification using a support vector machine (SVM). However, this approach focused primarily on attention state representation and did not fully leverage frequency-specific evoked responses inherent to SSVEP/SSMVEP paradigms. [PERSON] et al. [22] introduced a feature fusion technique combining task-related component analysis (TRCA) coefficients and power spectral density (PSD) features into a composite TRPSD descriptor, achieving enhanced performance with a stepwise linear discriminant analysis (SWLDA) classifier. Despite the progress in feature fusion techniques, many existing methods still rely on either spatial or frequency domain features alone, overlooking the benefits of their integration. Furthermore, the lack of consideration for the compatibility between specific feature types and classifier architectures often leads to suboptimal model performance.