Extending Brain-Computer Interface Access with a Multilingual Language Model in the P300 Speller

Brain-computer interfaces (BCI) such as the P300 speller have the potential to restore communication to advanced-stage neuromuscular disease patients. Research has improved typing speed and accuracy through innovations including the use of language models. While significant advances have been made, implementations have largely been restricted to a single language, primarily English. It is unclear whether these improvements would extend to other languages that present potential technical hurdles due to different alphabets and grammatical structures. Here, we adapt a language model-based classifier designed for English to two other languages, Spanish and Greek, to demonstrate the generalizability of these methods. Online experimental trials with 30 healthy native English, Spanish, and Greek speakers showed no significant difference between performances using the different versions of the system (66.20 vs. 61.97 vs. 60.89 bits/minute). Extending these methods across languages allows for expanding access to BCI systems to other populations, particularly in the developing world.