{"ID":2833311,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.03458","arxiv_id":"2512.03458","title":"A Convolutional Framework for Mapping Imagined Auditory MEG into Listened Brain Responses","abstract":"Decoding imagined speech engages complex neural processes that are difficult to interpret due to uncertainty in timing and the limited availability of imagined-response datasets. In this study, we present a Magnetoencephalography (MEG) dataset collected from trained musicians as they imagined and listened to musical and poetic stimuli. We show that both imagined and perceived brain responses contain consistent, condition-specific information. Using a sliding-window ridge regression model, we first mapped imagined responses to listened responses at the single-subject level, but found limited generalization across subjects. At the group level, we developed an encoder-decoder convolutional neural network with a subject-specific calibration layer that produced stable and generalizable mappings. The CNN consistently outperformed the null model, yielding significantly higher correlations between predicted and true listened responses for nearly all held-out subjects. Our findings demonstrate that imagined neural activity can be transformed into perception-like responses, providing a foundation for future brain-computer interface applications involving imagined speech and music.","short_abstract":"Decoding imagined speech engages complex neural processes that are difficult to interpret due to uncertainty in timing and the limited availability of imagined-response datasets. In this study, we present a Magnetoencephalography (MEG) dataset collected from trained musicians as they imagined and listened to musical an...","url_abs":"https://arxiv.org/abs/2512.03458","url_pdf":"https://arxiv.org/pdf/2512.03458v1","authors":"[\"Maryam Maghsoudi\",\"Mohsen Rezaeizadeh\",\"Shihab Shamma\"]","published":"2025-12-03T05:23:10Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.LG\",\"cs.SD\",\"eess.AS\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}