A study presented at the IEEE Engineering in Medicine and Biology Society conference highlights advances in brain-computer interface (BCI) technology, particularly the steady-state visual evoked potential (SSVEP) BCI. This technology translates brain signals into commands for devices, offering potential rehabilitation for individuals with motor disabilities. However, some users, termed BCI illiterate, struggle to control these systems effectively. To address this, researchers implemented a user-centered approach using neurofeedback training (NFT) to enhance SSVEP-BCI performance.

After five days of training, participants in the NFT group showed significant improvements in controlling the BCI system, while the control group did not exhibit similar gains. Notably, some individuals previously classified as BCI illiterate were able to effectively use the system following the training. Analysis indicated that the enhanced performance was linked to increased power and phase coherence of SSVEP responses, suggesting that NFT improved users’ neural responses related to the task. These findings indicate NFT’s potential to mitigate BCI illiteracy and enhance the usability of SSVEP-BCI systems, especially for individuals with severe motor impairments, such as those with amyotrophic lateral sclerosis (ALS) or locked-in syndrome (LIS).