Text embeddings from CLIP and GPT-2 predict MEG responses to spoken stories better than audio features, with text effects strongest over frontal sensors and audio effects over lateral temporal sensors.
Mne-bids: Organizing electrophysiological data into the bids format and facilitating their analysis
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Bridging Auditory Perception and Language Comprehension through MEG-Driven Encoding Models
Text embeddings from CLIP and GPT-2 predict MEG responses to spoken stories better than audio features, with text effects strongest over frontal sensors and audio effects over lateral temporal sensors.