Improving Neural Decoding Performance for Language BCIs by Explicitly Modeling Context-Induced Noise

Language brain-computer interfaces (BCIs), including speech and handwriting BCIs, hold significant promise for restoring communication in individuals with paralysis. However, the performance of current language BCIs remains limited due to the non-stationarity of neural activity. Existing neural decoding approaches typically treat neural non-stationarity as stochastic noise. For language BCIs, however, the noise is more complex due to the influence of semantic context in language sequences. For example, the articulation of the same phoneme can vary subtly depending on its surrounding content. Such context-induced variations introduce additional noise into neural representations and can confuse neural decoders, yet this issue has been largely overlooked in previous studies. Here, we propose explicitly modeling these context-induced noises to enhance neural decoding performance in language BCIs. Our key insight is that, unlike stochastic noise, context-induced noise carries useful information that can help the decoder disambiguate linguistically similar units. Therefore, by separating and modeling context-induced noise, we achieve improved decoding performance for language BCIs. Experiments on multiple datasets demonstrate that our approach achieves state-of-the-art performance.