# MEG-to-MEG Transfer Learning and Cross-Task Speech/Silence Detection with Limited Data

arXiv: https://arxiv.org/abs/2602.18253 · HTML: https://mlsanity.com/paper/2602.18253/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 48 references that are in the map.
- First posted on arXiv 20 Feb 2026.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Decoding speech perception from non-invasive brain recordings](https://mlsanity.com/paper/2208.12266/) (2022)
- [Brain decoding: toward real-time reconstruction of visual perception](https://mlsanity.com/paper/2310.19812/) (2023)
- [Scaling laws for decoding images from brain activity](https://mlsanity.com/paper/2501.15322/) (2025)
- [Brain-to-Text Decoding: A Non-invasive Approach via Typing](https://mlsanity.com/paper/2502.17480/) (2025)
- [LibriBrain: Over 50 Hours of Within-Subject MEG to Improve Speech Decoding Methods at Scale](https://mlsanity.com/paper/2506.02098/) (2025)
- [The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset](https://mlsanity.com/paper/2506.10165/) (2025)
- [Unlocking Non-Invasive Brain-to-Text](https://mlsanity.com/paper/2505.13446/) (2025)
- [Scaling Law in Neural Data: Non-Invasive Speech Decoding with 175 Hours of EEG Data](https://mlsanity.com/paper/2407.07595/) (2024)
- [Decoding individual words from non-invasive brain recordings across 723 participants](https://mlsanity.com/paper/2412.17829/) (2024)
- [ConvConcatNet: a deep convolutional neural network to reconstruct mel spectrogram from the EEG](https://mlsanity.com/paper/2401.04965/) (2024)
- [EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding](https://mlsanity.com/paper/2506.19141/) (2025)
- [MEGConformer: Conformer-Based MEG Decoder for Robust Speech and Phoneme Classification](https://mlsanity.com/paper/2512.01443/) (2025)
- [Elementary, My Dear Watson: Non-Invasive Neural Keyword Spotting in the LibriBrain Dataset](https://mlsanity.com/paper/2510.21038/) (2025)
- [Decoding Phone Pairs from MEG Signals Across Speech Modalities](https://mlsanity.com/paper/2505.15355/) (2025)
- [MEGState: Phoneme Decoding from Magnetoencephalography Signals](https://mlsanity.com/paper/2512.17978/) (2025)
- [Transfer Learning from ImageNet for MEG-Based Decoding of Imagined Speech](https://mlsanity.com/paper/2601.15909/) (2026)

## Abstract (arXiv)

Data-efficient neural decoding is a central challenge for speech brain-computer interfaces. We present the first demonstration of transfer learning and cross-task decoding for MEG-based speech models spanning perception and production. We pre-train a Conformer-based model on 50 hours of single-subject listening data and fine-tune on just 5 minutes per subject across 18 participants. Transfer learning yields consistent improvements, with in-task accuracy gains of 1-4% and larger cross-task gains of up to 5-6%. Not only does pre-training improve performance within each task, but it also enables reliable cross-task decoding between perception and production. Critically, models trained on speech production decode passive listening above chance, confirming that learned representations reflect shared neural processes rather than task-specific motor activity.