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Brain Decoding

Motor Brain Decoding is fundamental task for building motor brain computer interfaces (BCI).

Progress in predicting finger movements based on brain activity allows us to restore motor functions and improve rehabilitation process of patients.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–10 of 118 papers

TitleStatusHype
A Pre-trained Framework for Multilingual Brain Decoding Using Non-invasive Recordings—0
Exploring The Visual Feature Space for Multimodal Neural DecodingCode0
ViEEG: Hierarchical Neural Coding with Cross-Modal Progressive Enhancement for EEG-Based Visual Decoding—0
Probability Distribution Alignment and Low-Rank Weight Decomposition for Source-Free Domain Adaptive Brain Decoding—0
Riemannian Geometry for the classification of brain states with intracortical brain-computer interfaces—0
Spatiotemporal Learning of Brain Dynamics from fMRI Using Frequency-Specific Multi-Band Attention for Cognitive and Psychiatric ApplicationsCode0
A Survey on fMRI-based Brain Decoding for Reconstructing Multimodal StimuliCode0
From Eye to Mind: brain2text Decoding Reveals the Neural Mechanisms of Visual Semantic Processing—0
SEED: Towards More Accurate Semantic Evaluation for Visual Brain Decoding—0
Diffusion Models for Computational Neuroimaging: A SurveyCode0
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All datasetsBCI Competition IV: ECoG to Finger MovementsStanford ECoG library: ECoG to Finger Movements

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FingerFlexPearson Correlation0.67—Unverified
2Gradient boosted trees on Riemannian featuresPearson Correlation0.53—Unverified
3CNN-LSTMPearson Correlation0.52—Unverified
4Multi purpose CNNPearson Correlation0.52—Unverified
5Linear regression based on band-specific ECoGPearson Correlation0.48—Unverified
6Interpretable Compact CNNPearson Correlation0.45—Unverified
7Switching linear modelsPearson Correlation0.43—Unverified