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LISA is a compact, interpretable MEG decoder that retrieves perceived speech from non-invasive brain recordings with 39.75% Top-1 accuracy among 1,005 candidates using ~20x fewer parameters, while revealing cortical sources and speech features.
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 introduces a multi-feature fusion framework for semantic reconstruction from non-invasive brain recordings, combining static lexical (Word2Vec) and dynamic contextual (GPT) representations via cross-attention, achieving state-of-the-art performance in brain-to-text decoding.
Meta open-sourced Brain2Qwerty v2, a non-invasive brain-to-text system using MEG signals and deep learning, achieving up to 78% word accuracy.
Meta has improved Brain2QWERTY, a non-invasive system using MEG and EEG to decode brain activity into text, enabling typing.
NeuralSet is a new Python package that provides fast, scalable preprocessing and embedding tools for multimodal neuro-AI data including fMRI, EEG, MEG, ECoG, spikes, plus text, audio, video and images.