SpectraLoom: Spectral-Attentive Multi-Scale Neural Decoding for Open-Vocabulary EEG-to-Text Translation

Open-vocabulary EEG-to-text decoding generates the sentence a participant reads from the electroencephalogram recorded at each fixated word, and is a step towards non-invasive brain–computer interfaces for communication. Existing decoders flatten the eight frequency bands of every word event into one vector, model temporal context at a single scale, and connect the EEG encoder to a pretrained language model through a fixed linear projection. We propose SpectraLoom, in which each component answers one of these limitations: (1) Spectral Band Attention (SBA) computes a word-specific gate over the eight band slices with 315 parameters and retains all 840 input features; (2) a multi-scale Conv1D–BiLSTM encoder combines parallel convolutions over three, five and seven neighbouring word events with sentence-wide bidirectional recurrence; and (3) a Cross-Attention Bridge (CAB) refines every word-aligned EEG state by attending over the whole sentence before it enters the BART encoder. We evaluate five systems trained under one protocol, with a sentence-grouped split, identical decoding and three seeds, on ZuCo 1.0 and zero-shot on ZuCo 2.0. SpectraLoom attains the highest ROUGE-L and the lowest word error rate on both corpora (16.80 and 97.55 in domain, 17.65 and 92.70 zero-shot) with 44% fewer parameters than T5-large, and it attains the highest teacher-forced BLEU-1 to BLEU-4. Over three seeds, removing or simplifying any component lowers BLEU-1 to BLEU-4 and ROUGE-L on both corpora, and a word-independent band gate costs 4.87 BLEU-1. Across three seeds, its BLEU-1 varies with a standard deviation of 0.74, against 3.35 for BrainTranslator with the same BART-large backbone. Code is available at https://github.com/anonymoususer98700/spectraloom.