A Deep Learning Approach to Artifact Removal in Transcranial Electrical Stimulation: From Shallow Methods to Deep Neural Networks and State Space Models
Published in Journal of Neuroscience, 2025
This paper presents a novel approach to denoising EEG signals contaminated by artifacts from transcranial electrical stimulation (tES) using deep learning techniques. We explore various architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and state space models, to effectively separate neural signals from stimulation-induced artifacts.