NeuroLens: Cross-Modal EEG-to-Text Decoding via Structured State Space Encoding and Contrastive Alignment

Open-vocabulary EEG-to-text decoding generates the sentence a participant is reading from the neural recording alone, and underpins assistive communication and brain–computer interfaces. Progress on this task has come from stronger EEG encoders paired with a pretrained language decoder, yet once evaluation is made rigorous, with sentence-disjoint splits and no teacher forcing, absolute scores are low and it is unclear how much of each score is read from the brain signal and how much is supplied by the decoder's language prior. We identify three limitations of current systems: the encoders in use are poorly matched to short, noisy, word-level EEG sequences; the token-level objective is satisfied by fluent text whether or not the decoder reads the signal; and the contribution of the language prior is neither controlled nor measured. We propose NeuroLens, in which each component answers one of the above limitations: (1) a bidirectional S4D encoder with HiPPO-initialised long-range memory; (2) a reliance-aware objective that combines shuffled-EEG discrimination, contrastive alignment and a vocabulary prior, so that dependence on the paired recording is rewarded directly; and (3) decoder prior dampening, a tunable coefficient on decoder self-attention, together with a verification protocol that replaces the EEG input with shuffled, noise and zero signals over three seeds. On ZuCo 1.0 under a sentence-disjoint protocol without teacher forcing, NeuroLens reaches BLEU-1 of 22.44 and BLEU-4 of 1.46, improving on the previous state-of-the-art by 92% on BLEU-4, and its BLEU-4 falls by 68% when the EEG is shuffled across sentences and by 89% when it is zeroed, which shows that the outputs depend on the recorded signal. A matched-protocol ablation and a dampening sweep identify which components carry the gain and which do not. Code is available at https://github.com/anonymous399764/AnonymousEEG2TEXT.