EEG to Text

EEG-to-text research aims to decode human language directly from brainwave patterns recorded via electroencephalography (EEG), enabling a non-invasive brain-computer interface for communication. Current efforts focus on improving the accuracy of open-vocabulary text generation using advanced neural network architectures like transformers, often incorporating pre-trained language models and contrastive learning techniques to bridge the semantic gap between EEG signals and textual representations. This field holds significant promise for assisting individuals with communication impairments, but challenges remain in achieving high accuracy and robustness across diverse subjects and contexts, necessitating rigorous evaluation methodologies.