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Transformer-based Spatial-Temporal Feature Learning for EEG Decoding

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arxiv 2106.11170 v1 pith:VLWJ42TM submitted 2021-06-11 eess.SP cs.AIcs.LG

Yonghao Song , Xueyu Jia , Lie Yang , Longhan Xie This is my paper · ORCID
classification eess.SPcs.AIcs.LG
keywords attentiondatadecodingtimetransformingcnnsdimensionfeatures
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At present, people usually use some methods based on convolutional neural networks (CNNs) for Electroencephalograph (EEG) decoding. However, CNNs have limitations in perceiving global dependencies, which is not adequate for common EEG paradigms with a strong overall relationship. Regarding this issue, we propose a novel EEG decoding method that mainly relies on the attention mechanism. The EEG data is firstly preprocessed and spatially filtered. And then, we apply attention transforming on the feature-channel dimension so that the model can enhance more relevant spatial features. The most crucial step is to slice the data in the time dimension for attention transforming, and finally obtain a highly distinguishable representation. At this time, global averaging pooling and a simple fully-connected layer are used to classify different categories of EEG data. Experiments on two public datasets indicate that the strategy of attention transforming effectively utilizes spatial and temporal features. And we have reached the level of the state-of-the-art in multi-classification of EEG, with fewer parameters. As far as we know, it is the first time that a detailed and complete method based on the transformer idea has been proposed in this field. It has good potential to promote the practicality of brain-computer interface (BCI). The source code can be found at: \textit{https://github.com/anranknight/EEG-Transformer}.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AdaBrain-Bench: Benchmarking Brain Foundation Models for Brain-Computer Interface Applications

    cs.LG 2025-07 conditional novelty 6.0 of 10

    AdaBrain-Bench evaluates four EEG foundation models and four traditional baselines on 13 datasets and finds pretrained models, especially LaBraM and CBraMod, generally win on cross-subject and few-shot transfer.

  2. CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding

    cs.HC 2025-06 conditional novelty 6.0 of 10

    CSBrain, a cross-scale spatiotemporal EEG foundation model, reports state-of-the-art results across 11 tasks and 16 datasets, but its claim of consistent superiority over all baselines is not supported by its own tables.

  3. DiffEEG: A Self-Supervised Denoising Diffusion Model for Learning EEG Generic Representations

    cs.LG 2026-07 conditional novelty 5.5 of 10

    A diffusion U-Net pretrained on unlabeled TUHSZ EEG plus an F1-maximizing RL decision layer yields clinically usable patient-wise seizure detection and subtyping under severe class imbalance.

  4. Interpretable EEG biomarkers with bag-of-waves: Spatial and temporal waveform dictionaries for low-data regimes

    cs.LG 2026-07 conditional novelty 5.0 of 10

    A small unsupervised EEG waveform dictionary with transition counts matches deep-model performance on three tasks while staying interpretable.

  5. MSBraM: A Multi-scale Self-supervised Brain Foundation Model for Hierarchical EEG Dynamics Learning

    cs.AI 2026-07 conditional novelty 5.0 of 10

    A multi-scale, VQ-VAE-based EEG foundation model with curriculum masking beats prior EEG foundation models on 11 of 12 benchmarks, but two test sets overlap with its pretraining corpus.

  6. BEAM: Brainwave Empathy Assessment Model for Early Childhood

    cs.LG 2025-09 conditional novelty 5.0 of 10

    BEAM, a multi-view EEG deep learning model, predicts high vs low empathy in 4-6 year olds with 64.7% accuracy and 0.008 standard deviation on 57 children.

  7. Dynamic Survival Prediction using Longitudinal Images based on Transformer

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    A Transformer combining vision and sequence encoders with a Cox survival head for dynamic survival prediction from longitudinal MRI, evaluated on Alzheimer's disease data.

  8. AFPM: Alignment-based Frame Patch Modeling for Cross-Dataset EEG Decoding

    cs.HC 2025-07 conditional novelty 5.0 of 10

    A calibration-free EEG decoding framework that aligns and patches multi-dataset signals so a pretrained model can decode new users without labeled tuning.

  9. CRIA: A Cross-View Interaction and Instance-Adapted Pre-training Framework for Generalizable EEG Representations

    cs.LG 2025-06 conditional novelty 5.0 of 10

    Fusing temporal, spectral, and spatial EEG views with cross-attention and view-wise masking improves downstream classification and cross-dataset generalization over prior EEG pretraining models.

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