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This paper introduces Brain2Semantics2Text, a non-invasive speech decoding method that maps MEG responses to semantic embeddings to reconstruct sentence-level text without word-level alignment.
The article announces the 2026 PNPL Competition focused on word classification and efficient cross-subject generalization in the LibriBrain100 dataset, advancing non-invasive speech decoding for brain-computer interfaces.
The paper introduces LibriBrain100, a large-scale MEG dataset with over 100 hours of data for neural speech decoding, achieving state-of-the-art performance on word classification benchmarks.
This research uses non-invasive EEG and contrastive learning to decode words during silent reading, showing scalable lexical information recovery that scales with data volume.
This paper presents an interpretable MEG-to-audio retrieval model for perceived speech, redesigned with spherical-harmonics spatial attention and source mapping, achieving 39.75% Top-1 accuracy with far fewer decoder parameters while revealing which speech features drive retrieval.
This paper investigates the effects of phonetic versus character targets and selective state-space models (Mamba) for intracortical brain-to-text decoding, finding that a GRU-based recurrent decoder remains the strongest performer on the Brain-to-Text '25 benchmark.
A model trained on ultrasound recordings of the tongue can decode silent speech with a 15.6% word error rate, approaching lip-reading performance despite a smaller dataset.
A new non-invasive method translates brain waves into words, offering a communication pathway without the need for surgery.
Casey Harrell, a man with ALS, has become the first 'power user' of a speech brain-computer interface, using it independently for thousands of hours to speak, browse the web, and work. A study in Nature Medicine reports the device's long-term reliability and expanded capabilities.