JPEG artifacts removal based on quantization coefficients.

ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

MWF-based EEG artifact removal in MATLAB

A test script for ARCNN powered by PyTorch.

Detect EEG artifacts, outliers, or anomalies using supervised machine learning.

Algorithms and evaluation toolkit for removing strong cardiac interference from surface EMG measurements

Stimulation Artifact Removal

sharing code and data for artifact removal in EEG

Python port of the PARRM algorithm for removing periodic artefacts from signals.

computational-pathology-pipeline

Python modules for removal of periodic artifacts, even when non-stationary and non-sinusoidal. Developed with application for tACS-EEG in mind.

Cross-component video artifact filtering experiment for Vapoursynth.

Periodic artifact removal algorithms that can remove periodic artifacts in the presence of unknown phase shifts and with applications to deep brain stimulation.

Matlab modules for removal of periodic artifacts, even when non-stationary and non-sinusoidal. Developed with application for tACS-EEG in mind.

EEG signal preprocessing is essential for improving the quality of brain signals by removing noise and artifacts. It helps isolate meaningful features, making the data more suitable for analysis and classification. This step significantly boosts the performance and adaptability of BCI systems in real-world scenarios.

Automated Python-based Resting-State EEG Preprocessing

eegFloss is a Python package that can identify artifacts in sleep EEG signals and detect data usability for sleep autoscoring using a machine learning model called eegUsability.