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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 101–118 of 118 papers

TitleStatusHype
DreamCatcher: Revealing the Language of the Brain with fMRI using GPT Embedding—0
Dual Stream Graph Transformer Fusion Networks for Enhanced Brain Decoding—0
Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencoder—0
Explainable fMRI-based Brain Decoding via Spatial Temporal-pyramid Graph Convolutional Network—0
FAASTA: A fast solver for total-variation regularization of ill-conditioned problems with application to brain imaging—0
Fast and accurate decoding of finger movements from ECoG through Riemannian features and modern machine learning techniques—0
FAST functional connectivity implicates P300 connectivity in working memory deficits in Alzheimer's disease—0
From Eye to Mind: brain2text Decoding Reveals the Neural Mechanisms of Visual Semantic Processing—0
Generating Visual Stimuli from EEG Recordings using Transformer-encoder based EEG encoder and GAN—0
Gradient Hyperalignment for multi-subject fMRI data alignment—0
Hierarchical Multi-resolution Mesh Networks for Brain Decoding—0
Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features—0
High-Dimensional Classification for Brain Decoding—0
How Many Bytes Can You Take Out Of Brain-To-Text Decoding?—0
Interpretability in Linear Brain Decoding—0
Interpreting wide-band neural activity using convolutional neural networks—0
Learning Deep Temporal Representations for Brain Decoding—0
MEG Decoding Across Subjects—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