Semantic Alignment of EEG and Text: A Contrastive Learning Framework for Decoding Imagined Speech

Imagined speech decoding from EEG offers a promising route toward naturalistic brain-computer interfaces. Traditional closed-set classifiers treat semantic categories as independent labels and do not explicitly exploit relationships among linguistic intents, limiting output flexibility.