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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 51–60 of 118 papers

TitleStatusHype
Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)—0
Modeling the Sequence of Brain Volumes by Local Mesh Models for Brain Decoding—0
Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection in-hospital—0
Multi-view and Cross-view Brain Decoding—0
Natural Image Reconstruction from fMRI using Deep Learning: A Survey—0
Neuro-Vision to Language: Enhancing Brain Recording-based Visual Reconstruction and Language Interaction—0
On Creating A Brain-To-Text Decoder—0
On the benefits of self-taught learning for brain decoding—0
Predicting Classification Accuracy When Adding New Unobserved Classes—0
Probability Distribution Alignment and Low-Rank Weight Decomposition for Source-Free Domain Adaptive Brain Decoding—0
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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
3Multi purpose CNNPearson Correlation0.52—Unverified
4CNN-LSTMPearson 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
#ModelMetricClaimedVerifiedStatus
1FingerFlexPearson Correlation0.49—Unverified