EEG Denoising
4 papers with code • 0 benchmarks • 0 datasets
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Latest papers with no code
A multi-artifact EEG denoising by frequency-based deep learning
These signals are typically a combination of neurological activity and noise, originating from various sources, including physiological artifacts like ocular and muscular movements.
Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-heuristically Optimized Non-local Means Filter
A novel multi-stage EEG denoising method is proposed for the first time in which wavelet packet decomposition (WPD) is combined with a modified non-local means (NLM) algorithm.
Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN AutoEncoder
The study shows that the proposed fractional and compressed architecture performs better than existing state-of-the-art signal denoising methods.
Deep learning denoising for EOG artifacts removal from EEG signals
In the proposed scheme, we convert each EEG signal to an image to be fed to a U-NET model, which is a deep learning model usually used in image segmentation tasks.
Denoising Time Series Data Using Asymmetric Generative Adversarial Networks
This step is especially important if the noise in data originates from diverse sources.
