EEG Feature Extraction and Classification of SSVEP

As an innovative communication method, brain-computer interface (BCI) can directly convert human brain activity into control signals, which is of great significance in improving the quality of life of people with disabilities. In this paper, the application of steady-state visual evoked potential (SSVEP) in BCI is discussed, and the feature extraction and classification methods of EEG signals are studied. Feature extraction of EEG signals was performed by preprocessing them using the EEGLAB toolbox and classification using support vector machine (SVM) to identify different patterns of EEG activity. The experimental results show that this feature extraction and classification method significantly improves the performance of BCI system. Future research can further optimize the feature extraction algorithm and improve the visual stimulation paradigm to improve the recognition accuracy and practicality of the system. Additionally, integrating advanced machine learning techniques such as deep learning and transfer learning could potentially enhance the system’s ability to adapt to individual users and generalize across different tasks, thereby increasing the robustness and versatility of the BCI system in real-world applications.