A Convolutional Neural Network for the Removal of Simultaneous Ocular and Myogenic Artifacts from EEG Signals

Electroencephalography (EEG) is a non-invasive technique widely used in neuroscience to diagnose neural disorders and analyse brain activity. However, ocular and myogenic artifacts from eye movements and facial muscle activity often contaminate EEG signals, compromising signal analysis accuracy. While deep learning models are a popular choice for denoising EEG signals, most focus on removing either ocular or myogenic artifacts independently. This paper introduces a novel EEG denoising model capable of handling the simultaneous occurrence of both artifacts. The model uses convolutional layers to extract spatial features and a fully connected layer to reconstruct clean signals from learned features. The model integrates the Adam optimiser, average pooling, and ReLU activation to effectively capture and restore clean EEG signals. It demonstrates superior performance, achieving low training and validation losses with a significantly reduced (Formula presented.) value of 0.35 in both the temporal and spectral domains. A high cross-correlation coefficient of 0.94 with ground-truth EEG signals confirms the model’s fidelity. Compared to the existing architectures and models (FPN, UNet, MCGUNet, LinkNet, MultiResUNet3+, Simple CNN, Complex CNN) across a range of signal-to-noise ratio values, the model shows superior performance for artifact removal. It also mitigates overfitting, underscoring its robustness in artifact suppression.