MEBM-Speech: Multi-scale Enhanced BrainMagic for Robust MEG Speech Detection
Authors: Li Songyi, Zheng Linze, Liang Jinghua, Zhang Zifeng
Organizations: Speech and Hearing Research Center, School of Intelligence Science and Technology, Peking University, Beijing, China 100871 · Center for BioMed-X Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China 100871
We propose MEBM-Speech, a multi-scale enhanced neural decoder for speech activity detection from non-invasive magnetoencephalography (MEG) signals. Built upon the BrainMagic backbone, MEBM-Speech integrates three complementary temporal modeling mechanisms: a multi-scale convolutional module for short-term pattern extraction, a bidirectional LSTM (BiLSTM) for long-range context modeling, and a depthwise separable convolutional layer for efficient cross-scale feature fusion. A lightweight temporal jittering strategy and average pooling further improve onset robustness and boundary stability. The model performs continuous probabilistic decoding of MEG signals, enabling fine-grained detection of speech versus silence states - an ability crucial for both cognitive neuroscience and clinical applications. Comprehensive evaluations on the LibriBrain Competition 2025 Track1 benchmark demonstrate strong performance, achieving an average F1 macro of 89.3% on the validation set and comparable results on the official test leaderboard. These findings highlight the effectiveness of multi-scale temporal representation learning for robust MEG-based speech decoding.
Figures & tables
Figure 1: Overall architecture of the proposed MEBM-Speech model. (a) The complete processing pipeline. (b) The spatial attention module enhances sensor-level representations by learning spatial relevance weights across MEG channels. (c) The BM encoder extracts mid-term contextual features from spatially weighted signals. (d) The short-term multi-scale convolutional module captures fine-grained temporal dependencies using multiple receptive fields. (e) The depthwise separable convolutional layer further refines temporal representations with lightweight channel-wise and pointwise filtering.
Model Variant
F1 macro (%)
Acc macro (%)
Full Model
89.34±0.24
89.25±0.37
w/o BM Encoder
88.36±0.13
88.29±0.24
w/o Multi-scale Conv
89.17±0.19
88.98±0.28
w/o BiLSTM
89.21±0.17
89.27±0.22
w/o BM Encoder + Multi-scale Conv
87.91±0.13
87.76±0.38
w/o Multi-scale Conv + BiLSTM
89.17±0.21
89.20±0.24
Table 1: Results and ablation analysis on the local validation set under six random seeds (0–5). Metrics include F1 macro and Acc macro (mean ± std).
We propose MEBM-Phoneme, a multi-scale enhanced neural decoder for phoneme classification from non-invasive magnetoencephalography (MEG) signals. Built upon the BrainMagic backbone, MEBM-Phoneme integrates a short-term multi-scale convolutional module to augment the native mid-term encoder, with fused representations via depthwise separable convolution for efficient cross-scale integration. A convolutional attention layer dynamically weights temporal dependencies to refine feature aggregation. To address class imbalance and session-specific distributional shifts, we introduce a stacking-based local validation set alongside weighted cross-entropy loss and random temporal augmentation. Comprehensive evaluations on LibriBrain Competition 2025 Track2 demonstrate robust generalization, achieving competitive phoneme decoding accuracy on the validation and official test leaderboard. These results underscore the value of hierarchical temporal modeling and training stabilization for advancing MEG-based speech perception analysis.
Liang Jinghua, Zhang Zifeng, Li Songyi +1
Speech and Hearing Research Center, School of Intelligence Science and Technology, Peking University, Beijing, China 100871 · Center for BioMed-X Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China 100871
Decoding speech from non-invasive brain signals is challenging. For the LibriBrain 2025 Speech Detection task, we propose a novel two-step framework that bypasses direct reconstruction. First, a contrastive learning model retrieves the matching speech segment for the given test MEG from a large-scale audio library (LibriVox). Second, a speech detection model generates the binary silence/speech sequence directly from this retrieved audio. With this approach, our team Sherlock Holmes achieved first place in the extended track (F1-score: 0.962), demonstrating that leveraging external audio databases is a highly effective strategy.
Boda Xiao, Bo Wang, Heping Cheng
Center for BioMed-X Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China · Speech and Hearing Research Center, School of Intelligence Science and Technology, Peking University, Beijing, China · National Biomedical Imaging Center, State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China
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.
Xabier de Zuazo, Vincenzo Verbeni, Eva Navas +3
HiTZ Center, University of the Basque Country – UPV/EHU, Spain · Basque Center on Cognition, Brain and Language – BCBL, Spain · Laboratory of Functional Anatomy, Faculty of Human Motor Sciences, Université libre de Bruxelles (ULB), Belgium +3