Denoising EEG

This project applies advanced deep learning techniques to denoise EEG (Electroencephalography) signals, enhancing the quality of EEG data by removing noise and improving signal clarity. The LU-Net deep learning model is used to remove noise and artifacts, including EOG (Electrooculography) and EMG (Electromyography) signals. The model is trained using the EEGdenoiseNet dataset, and the denoised EEG data is later used for classification tasks for Alzheimer's disease detection. Performance is evaluated using metrics like correlation (corr) and relative root mean square error (RRMSE).