Steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs) offer a promising solution for facilitating communication in individuals with severe motor disabilities. Improving recognition accuracy through neural-network-based techniques is essential for developing more reliable and responsive SSVEP-based BCIs. In this study, HybridNet integrating a multiscale block (MSB), a residual attention network (ResAttNet), and a multitask learning (MTL) block is developed to improve the SSVEP-based BCI performance. In this study, we enhance the sensing performance of EEG-based BCIs by improving feature extraction and classification accuracy using EEG sensor data. MSB is used to capture diverse spectral patterns for robust feature extraction. ResAttNet is designed for adaptive channel-spatial feature enhancement. The MTL module further promotes shared feature learning across tasks to improve the classification accuracy. Experiments on two benchmark SSVEP datasets show that HybridNet achieves accuracies of up to 87.0 and 89.1% using 1 s EEG segments, outperforming other approaches. In future work, we will explore real-time and subject-independent implementations through integration with advanced EEG sensing technologies, enhancing signal robustness and practical applicability.