A Novel Approach to Improve SSVEP-BCI Performance Through Neurofeedback Training

Brain-Computer interface (BCI), which translates neural activities into commands for external devices, holds significant promise for clinical rehabilitation and assisted movement for individuals with motor disabilities. Among various BCI paradigms, the steady-state visual evoked potential (SSVEP) based BCI garnered considerable attention due to its relatively stable and high-speed communication capabilities. However, a notable portion of the population, referred to as BCI illiteracy, struggles to effectively control BCI systems due to their inability to generate or modulate the neural patterns required for interaction. To address this issue, we proposed a user-centered approach using neurofeedback training (NFT) to improve individual's performance on SSVEP-BCI. As a result, after a five-day training period, significant improvements in SSVEP-BCI performance were only observed in the training group rather than the control group without training. Notably, some subjects initially determined as BCI-illiterate also gained effective control of the BCI system after training. Further analysis revealed that the improvement of SSVEP-BCI performance had a close link with increased power and inter-trial phase coherence of the SSVEP response, indicating that NFT successfully strengthened the user's task-related neural responses. These findings highlight the potential of NFT as a user-centered intervention to improve BCI control performance, offering a promising pathway to address BCI illiteracy and promote the broader application of BCI systems.Clinical Relevance- This study proposes an effective approach to enhancing the controllability of SSVEP-BCI systems, addressing the critical issue of individual control limitations. The developed method demonstrates significant clinical potential for promoting SSVEP-BCI applications, particularly in facilitating communication and device control for patients with severe motor impairments, such as amyotrophic lateral sclerosis (ALS) and locked-in syndrome (LIS).