An Investigation of a Brain-Computer Interaction System for Steady-State Visual Evoked Potentials

Steady-state visual evoked potential (SSVEP), as a non-invasive EEG signal, is widely used in brain-computer interfaces (BCI) due to its high signal-to-noise ratio and high temporal resolution. With advancements in neural engineering, SSVEP-BCI technology has become one of the important research directions in the fields of medicine, rehabilitation and human-computer interaction. Despite its potential, SSVEP-BCI systems still face challenges such as signal stability, individual variability, system portability and interaction naturalness. This paper explores the relationship between SSVEP and brain function, analyzes its role in cognitive tasks, and evaluates the mechanism, current status, and challenges of SSVEP-BCI systems across various fields. By reviewing relevant literature, it examines the mechanism of SSVEP generation, its application in cognitive neuroscience, and the integration of SSVEP-BCI with other EEG signals (e.g., P300, motor imagery (MI), and electrooculogram (EOG)). Studies have indicated that the SSVEP-BCI system has high application value in brain-computer interaction, especially in the medical field, where it has been widely used in the rehabilitation of patients with movement disorders and the control of assistive devices. However, current studies face challenges such as decoding accuracy, system stability, and individual variability. By focusing on multimodal integration, deep learning, and wearables, future SSVEP-BCI developments aim to enhance accuracy, stability, and user experience, broadening their applications in medicine, industry, and entertainment.