EEG Denoising
5 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in EEG Denoising
Most implemented papers
EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising
ncclabsustech/EEGdenoiseNet
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Here, we present EEGdenoiseNet, a benchmark EEG dataset that is suited for training and testing deep learning-based denoising models, as well as for performance comparisons across models.
Improved robust weighted averaging for event-related potentials in EEG
The areas of improvement include significantly lower averaging error (45% lower RMSE and 37% lower maximum difference than for original implementation) and increased robustness to local minima, strong outliers and corrupted epochs common to real-life EEG signals, especially from low-cost devices.
Embedding Decomposition for Artifacts Removal in EEG Signals
ncclabsustech/deepseparator
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DeepSeparator employs an encoder to extract and amplify the features in the raw EEG, a module called decomposer to extract the trend, detect and suppress artifact and a decoder to reconstruct the denoised signal.
DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature Reuse
Finally, a Decoder layer is employed to reconstruct the artifact-reduced EEG signal.
EEGDiR: Electroencephalogram denoising network for temporal information storage and global modeling through Retentive Network
woldier/EEGDiR
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Electroencephalogram (EEG) signals play a pivotal role in clinical medicine, brain research, and neurological disease studies.
