SHINE: Sequential Hierarchical Integration Network for EEG and MEG

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[1]
Contrastive representation learning with transformers for robust auditory EEG decoding
2025Lies Bollens, Bernd Accou et al.
[2]
DMF2Mel: A Dynamic Multiscale Fusion Network for EEG-Driven Mel Spectrogram Reconstruction
2025Cunhang Fan, Sheng Zhang et al.
[3]
The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset
2025Gilad Landau, Miran Ozdogan et al.
[4]
LibriBrain: Over 50 Hours of Within-Subject MEG to Improve Speech Decoding Methods at Scale
2025Miran Özdogan, Gilad Landau et al.
[5]
SSM2Mel: State Space Model to Reconstruct Mel Spectrogram from the EEG
2025Cunhang Fan, Sheng Zhang et al.
[6]
Seeing helps hearing: A multi-modal dataset and a mamba-based dual branch parallel network for auditory attention decoding
2025Cunhang Fan, Hongyu Zhang et al.
[7]
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2025Lies Bollens, Corentin Puffay et al.
[8]
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2024Zelin Qiu, Dingding Yao et al.
[9]
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2024Qinke Ni, Hongyu Zhang et al.
[10]
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2024Xiran Xu, Bo Wang et al.
[11]
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2024Zelin Qiu, Jianjun Gu et al.
[12]
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2024Yuan Fang, Hao Li et al.
[13]
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2024Xiran Xu, Bo Wang et al.
[14]
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2024Bo Wang, Xiran Xu et al.
[15]
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2023Iustina Rotaru, Simon Geirnaert et al.
[16]
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2023Xiran Xu, Bo Wang et al.
[17]
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2023Zelin Qiu, Jianjun Gu et al.
[18]
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[19]
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Showing 20 of 26 references

Founder's Pitch

"Develop a neural network model, SHINE, for reconstructing speech-sequence data from MEG signals."

Brain-Computer InterfacesScore: 5View PDF ↗

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5

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2.5

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