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Brain Decoding On Bci Competition Iv Ecog To
Metrics
Pearson Correlation
Results
Performance results of various models on this benchmark
| Paper Title | Repository | ||
|---|---|---|---|
| FingerFlex | 0.67 | FingerFlex: Inferring Finger Trajectories from ECoG signals | |
| Gradient boosted trees on Riemannian features | 0.53 | Fast and accurate decoding of finger movements from ECoG through Riemannian features and modern machine learning techniques | - |
| CNN-LSTM | 0.52 | Decoding of finger trajectory from ECoG using deep learning | - |
| Multi purpose CNN | 0.52 | Interpreting wide-band neural activity using convolutional neural networks | - |
| Linear regression based on band-specific ECoG | 0.48 | Decoding finger flexion from band-specific ECoG signals in humans | - |
| Interpretable Compact CNN | 0.45 | Decoding and interpreting cortical signals with a compact convolutional neural network | - |
| Switching linear models | 0.43 | Decoding finger movements from ECoG signals using switching linear models | - |
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