A Comparative Review of Detection Methods in SSVEP-Based Brain-Computer Interfaces

Steady-state visually evoked potential (SSVEP) refers to the brain’s response to visual stimuli at different frequencies and is widely used in brain-computer interfaces (BCIs). Despite their potential, SSVEP-based BCIs face significant challenges in real-world applications, particularly in controlling assistive devices, prosthetics, and communication systems for individuals with disabilities. The challenges include suboptimal frequency detection accuracy and long calibration periods, which limit the effectiveness of SSVEPs and contribute to increased visual fatigue during extended sessions. This review addresses these challenges by offering an overview of feature extraction methods for SSVEP recognition. It includes mathematical explanations of the processes, highlights their strengths and limitations, compares them, and discusses future directions. Feature extraction techniques can be categorized into three groups: calibration-free, calibration-based, and deep learning. While calibration-free methods require minimal data, they typically achieve lower accuracy than calibration-based methods, which rely on training datasets to provide better accuracy and information transfer rates; however, the lengthy training sessions often make these algorithms unsuitable for everyday use. On the other hand, deep learning approaches have improved accuracy and adaptability by automatically extracting complex features from data and accommodating varying conditions, even with shorter time windows. However, they require large amounts of data for training to improve accuracy. To address this issue, both calibration-based and deep learning methods can benefit from transfer learning, which alleviates the need for extensive training data by sharing knowledge across subjects. This approach enhances recognition accuracy and reduces reliance on subject-specific training, ultimately making these methods more practical for real-world applications.