Thought-deciphering via hierarchical EEG-to-text alignment

Xiaopeng Mao, Morten Mørup, Xin Xu, Sang Wan Lee, Sadasivan Puthusserypady

Non-invasive brain-to-text interfaces offer alternative communication pathways for people with communication disabilities. However, sentence-level decoding from electroencephalography (EEG) remains challenging. We propose a hierarchical EEG-text alignment framework that retrieves sentences by progressively decoding coarse-grained semantic information and fine-grained sentence representations. We hypothesize that broader semantic context can be more reliably captured from EEG than individual sentences. Accordingly, EEG representations are first aligned with semantic classes, followed by sentence retrieval within the predicted class. Among over 2,000 unique text samples, our approach achieves average top-5 and top-10 retrieval accuracies of 62.6\% $\pm$ 1.0\% and 83.5\% $\pm$ 1.0\%, respectively, compared with 50.3\% $\pm$ 2.3\% and 68.3\% $\pm$ 2.0\% for direct EEG-sentence alignment. Qualitative analysis further reveals that decoding performance depends strongly on textual context. These findings demonstrate the potential of hierarchical semantic alignment for improving sentence-level EEG-to-text decoding.