EEG Artifact

EEG artifact removal aims to cleanse electroencephalography (EEG) signals of noise from sources like eye blinks, muscle movements, and environmental interference, improving the accuracy of brain activity analysis. Current research heavily utilizes deep learning, employing architectures such as convolutional neural networks (CNNs), transformers, and recurrent neural networks (RNNs like LSTMs), often combined with traditional methods like Independent Component Analysis (ICA), to achieve robust and automated artifact reduction. These advancements are crucial for enhancing the reliability of EEG-based research in neuroscience, neurology, and brain-computer interfaces, enabling more accurate interpretations of brain dynamics and improved diagnostic capabilities.