A preliminary study of steady-state visually-evoked potential-based non-invasive brain-computer interface technology as a communication aid for patients with amyotrophic lateral sclerosis.

Background: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that affects motor neurons, leading to severe disability and ultimately death. Communication difficulties are common in ALS patients as the disease progresses; thus, alternative communication aids need to be explored. This study sought to examine the use and effect of steady-state visually-evoked potential (SSVEP)-based non-invasive brain-computer interface (BCI) technology as a communication aid for patients with ALS and to examine possible influencing factors. Methods: In total, 12 patients with ALS were selected, and a 40-character target selection was performed using SSVEP-based non-invasive BCI technology. The patients were presented with specific visual stimuli, and nine-lead electroencephalogram (EEG) signals in the occipital region were acquired when the patients were looking at the target. Using the feature recognition analysis method, the final output was the characters recognized by the patients. The basic clinical data of the patients (e.g., age, gender, course of disease, affected area, and ALS functional scale score) were collected, and the BCI accuracy rate, information transmission rate, and average SSVEP recognition time were calculated. Results: The results revealed that the recognition efficiency of the ALS patients varied. The accuracy potential increased as the stimulus duration extended, highlighting the possibility for improvement via further optimization. The results also showed that the experimental design schedules typically used for healthy individuals may not be entirely suitable for ALS patients, which presents an exciting opportunity to tailor future studies to better meet the unique needs of ASL patients. Further, the results revealed the necessity of using customized experimental schedules in future studies, which could lead to more relevant and effective data collection for ALS patients. Conclusions: The study found that SSVEP-based non-invasive BCI technology has promising potential as a communication aid for ALS patients. While further algorithm optimization and comprehensive studies with larger sample sizes are necessary, the initial findings are encouraging, and could lead to the development of more effective communication solutions that are specifically tailored to address the challenges faced by ALS patients.