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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 61–70 of 118 papers

TitleStatusHype
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
Reconstructing Visual Stimulus Images from EEG Signals Based on Deep Visual Representation Model—0
Reconstruction of the External Stimuli from Brain Signals—0
Retinotopy Inspired Brain Encoding Model and the All-for-One Training Recipe—0
Riemannian Geometry for the classification of brain states with intracortical brain-computer interfaces—0
SEED: Towards More Accurate Semantic Evaluation for Visual Brain Decoding—0
Self-Supervised Pretraining on Paired Sequences of fMRI Data for Transfer Learning to Brain Decoding Tasks—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