## 1 Introduction  
Brain-computer interfaces (BCIs) enable individuals to control external devices using brain activity, bypassing the need for nerve or muscle involvement. EEG-based BCIs, which utilize electroencephalography (EEG) signals, are particularly popular due to their ease of use and wearability, making them suitable for a variety of applications [1, 2, 3, 4, 5, 6, 7, 8]. Among these, steady-state visual evoked potential (SSVEP)-based BCIs are particularly notable for their high signal-to-noise ratio (SNR) [3], positioning them as one of the most effective interface types. However, the performance of SSVEP-based BCIs often deteriorates in real-world settings, impacting their practical usability. Thus, enhancing classification performance is crucial to improve the transition of SSVEP-based BCIs from lab environments to real-world applications. The brain-computer interface (BCI) is a well-established technology that continues to evolve for advanced information communication and control [4, 5, 6, 7, 8, 9, 10]. A BCI enables direct communication between the human brain and a computer or external devices. It allows users to control and interact with devices using their thoughts and brain signals. Therefore, BCIs are especially suitable for developing assistive communication systems that can enable people with amyotrophic lateral sclerosis to communicate without relying on their muscles. In recent years, electroencephalography (EEG), which offers a non-invasive way to measure neural activity, has been extensively used for brain activity recording [11; 12; 13; 14; 15; 16; 17; 18; 19]. Therefore, developing a non-invasive EEG-based BCI is highly appropriate for real-life applications. A steady-state visual-evoked potential (SSVEP) is an oscillatory stimulus-response that is evoked by repetitive stimuli with a constant frequency, and it is used to elicit a brain response in the primary visual cortex. These responses are recorded in the EEG, and the frequency of the response matches the frequency of the flickering stimulus. Compared to other BCIs, an SSVEP-based BCI is a successful interface due to its ease of recording and high signal-to-noise ratio [3]. Therefore, an SSVEP-based BCI has been applied to various applications, such as visual spelling [20; 21] and decoding user intent to operate assistive devices [22; 23]. However, the accuracy of SSVEP-based BCIs plays a crucial role in their acceptance, and, therefore, improving their accuracy is very important for users to accept these BCIs. A suitable representation of the SSVEP signal is crucial for classification tasks, as it can significantly improve the system's performance. Multi-domain features (MDFs) are a suitable representation of the SSVEP signal as they can precisely represent its characteristics. Previous studies have used MDFs to increase the classification performance for various applications. [PERSON] et al. [24] applied the spatial, frequency, and compression domains to improve the classification performance for JPEG images. [PERSON] et al. [25] and [PERSON] et al. [26] used the time and frequency domains to enhance the performance of blood pressure prediction. [PERSON] et al. [27] selected a common spatial pattern, phase-locking value, Pearson correlation coefficient, and transfer entropy as the MDFs to improve the performance of the motor-modality-based BCI. The results showed that the MDFs are very useful, but the computational complexity is high in computing the features. Thus, selecting an MDF with low computational complexity is very useful for developing BCIs. [PERSON] et al. [28] used MDFs to diagnose faults in rolling element bearings. The experimental results of these studies showed that MDFs outperform single-domain features. Therefore, using MDFs can significantly improve the performance of SSVEP-based BCIs. Recently, multi-task learning (MTL), which leverages a shared representation to exploit commonalities across tasks, has been successfully used in many deep learning-based applications. [PERSON] et al.