Steady-State Visual Evoked Potential (SSVEP)-based Brain- Computer Interfaces (BCIs) are a promising tool for non-invasive neural communication and control, particularly for individuals with severe physical or medical conditions that limit conventional interaction. However, accurately detecting and classifying SSVEP signals remains challenging due to noise and inter-subject variability. This study evaluates the performance of established classification methods, including Canonical Correlation Analysis (CCA), Filter Bank CCA (FBCCA), and transfer learning models such as EEGNet, DeepConvNet, and ShallowConvNet. To address the limitations of existing methods, we propose a novel hybrid approach combining CCA, Continuous Wavelet Transform (CWT), and Convolutional Neural Networks (CNN). This method aims to enhance feature extraction and classification accuracy. The models were evaluated on the benchmark SSVEP dataset from Tsinghua University, with pre- processing steps involving independent component analysis (ICA) and band-pass filtering. FBCCA achieved the highest accuracy of 97.5%, followed by CCA (93%) and DeepConvNet (86.95%). Our proposed method attained an accuracy of 77.52%, demonstrating its potential for robust SSVEP classification. These results underline the value of advanced algorithms and preprocessing strategies in improving SSVEP-based BCI performance, paving the way for more effective assistive technologies.