Paper
SHINE: Sequential Hierarchical Integration Network for EEG and MEG
Authors
Xiran Xu, Yujie Yan, Xihong Wu, Jing Chen
Abstract
How natural speech is represented in the brain constitutes a major challenge for cognitive neuroscience, with cortical envelope-following responses playing a central role in speech decoding. This paper presents our approach to the Speech Detection task in the LibriBrain Competition 2025, utilizing over 50 hours of magnetoencephalography (MEG) signals from a single participant listening to LibriVox audiobooks. We introduce the proposed Sequential Hierarchical Integration Network for EEG and MEG (SHINE) to reconstruct the binary speech-silence sequences from MEG signals. In the Extended Track, we further incorporated auxiliary reconstructions of speech envelopes and Mel spectrograms to enhance training. Ensemble methods combining SHINE with baselines (BrainMagic, AWavNet, ConvConcatNet) achieved F1-macro scores of 0.9155 (Standard Track) and 0.9184 (Extended Track) on the leaderboard test set.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.23960v1</id>\n <title>SHINE: Sequential Hierarchical Integration Network for EEG and MEG</title>\n <updated>2026-02-27T12:12:03Z</updated>\n <link href='https://arxiv.org/abs/2602.23960v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.23960v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>How natural speech is represented in the brain constitutes a major challenge for cognitive neuroscience, with cortical envelope-following responses playing a central role in speech decoding. This paper presents our approach to the Speech Detection task in the LibriBrain Competition 2025, utilizing over 50 hours of magnetoencephalography (MEG) signals from a single participant listening to LibriVox audiobooks. We introduce the proposed Sequential Hierarchical Integration Network for EEG and MEG (SHINE) to reconstruct the binary speech-silence sequences from MEG signals. In the Extended Track, we further incorporated auxiliary reconstructions of speech envelopes and Mel spectrograms to enhance training. Ensemble methods combining SHINE with baselines (BrainMagic, AWavNet, ConvConcatNet) achieved F1-macro scores of 0.9155 (Standard Track) and 0.9184 (Extended Track) on the leaderboard test set.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.SD'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n <published>2026-02-27T12:12:03Z</published>\n <arxiv:comment>ranked second at LibriBrain Competition 2025 https://neural-processing-lab.github.io/2025-libribrain-competition/prizes/</arxiv:comment>\n <arxiv:primary_category term='cs.SD'/>\n <author>\n <name>Xiran Xu</name>\n </author>\n <author>\n <name>Yujie Yan</name>\n </author>\n <author>\n <name>Xihong Wu</name>\n </author>\n <author>\n <name>Jing Chen</name>\n </author>\n </entry>"
}