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Eeg Decoding

EEG Decoding - extracting useful information directly from EEG data.

Papers

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Showing 1–25 of 74 papers

TitleStatusHype
CBraMod: A Criss-Cross Brain Foundation Model for EEG DecodingCode3
CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery ClassificationCode3
Physics-inform attention temporal convolutional network for EEG-based motor imagery classificationCode2
DBConformer: Dual-Branch Convolutional Transformer for EEG DecodingCode2
Spatial Variation Generation Algorithm for Motor Imagery Data Augmentation: Increasing the Density of Sample VicinityCode1
fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationshipsCode1
Time-space-frequency feature Fusion for 3-channel motor imagery classificationCode1
MAtt: A Manifold Attention Network for EEG DecodingCode1
Deep learning with convolutional neural networks for EEG decoding and visualizationCode1
TCANet: A Temporal Convolutional Attention Network for Motor Imagery EEG DecodingCode1
Weight Freezing: A Regularization Approach for Fully Connected Layers with an Application in EEG ClassificationCode1
Subject-Aware Contrastive Learning for BiosignalsCode1
Enhancing Low-Density EEG-Based Brain-Computer Interfaces with Similarity-Keeping Knowledge DistillationCode1
MVCNet: Multi-View Contrastive Network for Motor Imagery ClassificationCode1
Transformer-based Spatial-Temporal Feature Learning for EEG DecodingCode1
EEG-CLIP : Learning EEG representations from natural language descriptionsCode1
ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model InterpretationCode1
Dataset Refinement for Improving the Generalization Ability of the EEG Decoding Model—0
A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding—0
Cross-paradigm pretraining of convolutional networks improves intracranial EEG decoding—0
Distributionally Robust Cross Subject EEG Decoding—0
Combining Euclidean Alignment and Data Augmentation for BCI decoding—0
An amplitudes-perturbation data augmentation method in convolutional neural networks for EEG decoding—0
Dual-TSST: A Dual-Branch Temporal-Spectral-Spatial Transformer Model for EEG Decoding—0
AFPM: Alignment-based Frame Patch Modeling for Cross-Dataset EEG Decoding—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BEIRAPearson Correlation0.44—Unverified