MEBM-Phoneme: Multi-scale Enhanced BrainMagic for End-to-End MEG Phoneme Classification

Liang Jinghua; Zhang Zifeng; Li Songyi; Zheng Linze

cs.SDFeb 27, 2026

MEBM-Phoneme: Multi-scale Enhanced BrainMagic for End-to-End MEG Phoneme Classification

Authors: Liang Jinghua, Zhang Zifeng, Li Songyi, Zheng Linze

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

Abstract

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.

Figures & tables

Figure 1: Overall architecture of the proposed MEBM-Phoneme 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 (%)	Top-3 Acc macro (%)	Top-5 Acc macro (%)
Full Model	60.95±0.90	89.54±0.48	95.08±0.61
w/o Weighted Loss	59.97±0.90	88.87±1.14	94.75±0.63
w/o Multi-scale Conv	59.75±0.68	88.98±1.12	94.67±1.03
w/o BM Encoder	54.43±2.07	84.96±1.69	92.19±1.28
w/o Conv. Attention	59.60±0.82	88.47±1.46	94.17±1.13

Table 1: Results and ablation analysis on the local validation set under six random seeds (0–5). Metrics include F1 macro , Top-3 Acc macro , and Top-5 Acc macro (mean ± std).

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Phoneme	/ey/	/ay/	/uh/	/uw/	/s/	/sh/	/m/	/ae/	/jh/	/ah/
Weight	0.05	3.00	10.00	3.00	0.80	3.00	3.00	3.00	1.50	2.00

Appendix

Table 2: Adaptive loss weights for each phoneme class.

Neural Decoding

Neural decoding maps brain activity or error-correction syndromes to stimuli, behavior, language, or corrective actions.

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MEBM-Speech: Multi-scale Enhanced BrainMagic for Robust MEG Speech Detection

Li Songyi, Zheng Linze, Liang Jinghua, Zhang Zifeng

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

Neural Decoding Speech Processing Multiscale Time-Series Modeling

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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.

Abstract

Neural Decoding

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