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

arXiv: https://arxiv.org/abs/2512.03458 · HTML: https://mlsanity.com/paper/2512.03458/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 3 Dec 2025.

## Builds on (most cited first, 2 of 2)

- [Decoding speech perception from non-invasive brain recordings](https://mlsanity.com/paper/2208.12266/) (2022)
- [MAD: Multi-Alignment MEG-to-Text Decoding](https://mlsanity.com/paper/2406.01512/) (2024)

## Built on by (most cited first, 3 of 3)

- [Zero-Shot Imagined Speech Decoding via Imagined-to-Listened MEG Mapping](https://mlsanity.com/paper/2605.08075/) (2026)
- [Mechanistic Interpretability of Brain-to-Speech Models Across Speech Modes](https://mlsanity.com/paper/2602.01247/) (2026)
- [Integrating Language Models into Listened and Imagined Speech Decoding from MEG](https://mlsanity.com/paper/2609.31997/) (2026)

## Abstract (arXiv)

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.