# SHINE: Sequential Hierarchical Integration Network for EEG and MEG

arXiv: https://arxiv.org/abs/2602.23960 · HTML: https://mlsanity.com/paper/2602.23960/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 43 references that are in the map.
- First posted on arXiv 27 Feb 2026.

## Builds on (most cited first, 16 of 16)

- [Decoding speech perception from non-invasive brain recordings](https://mlsanity.com/paper/2208.12266/) (2022)
- [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)
- [Relating EEG to continuous speech using deep neural networks: a review](https://mlsanity.com/paper/2302.01736/) (2023)
- [ConvConcatNet: a deep convolutional neural network to reconstruct mel spectrogram from the EEG](https://mlsanity.com/paper/2401.04965/) (2024)
- [DARNet: Dual Attention Refinement Network with Spatiotemporal Construction for Auditory Attention Detection](https://mlsanity.com/paper/2410.11181/) (2024)
- [A DenseNet-based method for decoding auditory spatial attention with EEG](https://mlsanity.com/paper/2309.07690/) (2023)
- [Convolutional Neural Networks Demystified: A Matched Filtering Perspective Based Tutorial](https://mlsanity.com/paper/2108.11663/) (2021)
- [Beware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals](https://mlsanity.com/paper/2405.17024/) (2024)
- [SSM2Mel: State Space Model to Reconstruct Mel Spectrogram from the EEG](https://mlsanity.com/paper/2501.10402/) (2025)
- [Auditory Attention Decoding from EEG using Convolutional Recurrent Neural Network](https://mlsanity.com/paper/2103.02183/) (2021)
- [Self-supervised speech representation and contextual text embedding for match-mismatch classification with EEG recording](https://mlsanity.com/paper/2401.04964/) (2024)
- [DMF2Mel: A Dynamic Multiscale Fusion Network for EEG-Driven Mel Spectrogram Reconstruction](https://mlsanity.com/paper/2507.07526/) (2025)
- [HappyQuokka System for ICASSP 2023 Auditory EEG Challenge](https://mlsanity.com/paper/2305.06806/) (2023)
- [DECAF: Dynamic Envelope Context-Aware Fusion for Speech-Envelope Reconstruction from EEG](https://mlsanity.com/paper/2602.19395/) (2026)
- [StreamAAD: Decoding Spatial Auditory Attention with a Streaming Architecture](https://mlsanity.com/paper/2408.13522/) (2024)

## Built on by (most cited first, 1 of 1)

- [MindVoice: Reconstructing Intelligible Speech from Non-invasive Neural Signals with Pretrained Priors](https://mlsanity.com/paper/2605.31173/) (2026)

## Abstract (arXiv)

How natural speech is represented in the brain constitutes a major challenge for cognitive neuroscience. Reconstructing the speech envelope and Mel spectrogram from EEG and MEG provides a time-resolved way to study its temporal and spectral structure. Speech-related neural activity spans sensors and temporal scales; extracting these representations while adapting the use of context to each acoustic target is a central problem in speech reconstruction. We propose SHINE, a Sequential Hierarchical Integration Network for EEG and MEG. A residual sensor adapter unifies input dimensions, intermediate dilated-block states retain temporal depth, and a target- and time-dependent gate fuses local hierarchical and attention-enhanced context predictions. Across two EEG and two MEG datasets, SHINE has the highest mean envelope and mean-Mel Pearson correlations among nine local baseline implementations on all eight dataset-metric combinations. SHINE also placed second in the speech-detection Extended Track of the NeurIPS 2025 PNPL Competition. Code will be released at https://github.com/xuxiran/SHINE.