Brain to Text

Brain-to-text technology aims to decode human thoughts and intentions directly into written language using brain signals, primarily to restore communication for individuals with severe speech impairments. Current research focuses on improving decoding accuracy and stability using advanced neural network architectures, such as transformer-based models and large language models, applied to both invasive (e.g., intracortical) and non-invasive (e.g., EEG, MEG) brain recordings. Significant progress has been made in achieving higher accuracy and longer-term stability, though challenges remain in expanding vocabulary size and reducing the need for frequent recalibration. This field holds immense potential for revolutionizing communication for people with neurological disorders and advancing our understanding of language processing in the brain.