A Convolutional Framework for Mapping Imagined Auditory MEG into Listened Brain Responses

• URL: http://arxiv.org/abs/2512.03458v1
• Date: Wed, 03 Dec 2025 05:23:10 GMT
• Title: A Convolutional Framework for Mapping Imagined Auditory MEG into Listened Brain Responses
• Authors: Maryam Maghsoudi, Mohsen Rezaeizadeh, Shihab Shamma,
• Abstract summary: We present a Magnetoencephalography (MEG) dataset collected from trained musicians as they imagined and listened to musical and poetic stimuli.<n>We show that both imagined and perceived brain responses contain consistent, condition-specific information.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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