Decoding Silent Reading: A Novel EEG-to-Text Method

Abstract

Brain Computer Interfaces (BCIs) that interpret neural signals into natural text have the potential to improve communication ability for individuals with speech disabilities. Despite early successes in this field, creating intelligible text from non-invasive EEG signals remains challenging due to the low SNR, high dimensionality of the EEG dataset across time, and the inherent non-stationarity in neural data. Current methods do not fully preserve the full meaning of speech because they rely on simple techniques like linear regression or phonetic mapping, which tend to miss the subtle meanings and context found in natural language. This paper presents EEG to Text, a contrastive latent space guided transformer architecture framework to reduce the semantic gap and increase the ability to reinterpret the relationship between neural patterns and text. EEG to Text creates a shared space that connects EEG signals and text data. It uses a strong, pre-trained text encoder as a guide to help align EEG features over time with the meaning of natural language. This creates a model that can identify meaningfully interpretable neural activity corresponding to linguistic meaning.